Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)

Permanent URI for this collectionhttps://hdl.handle.net/10347/34292

Centro referente de Galicia en IA, ten como misión a mellora da sociedade a través das tecnoloxías intelixentes e aspira a ser recoñecido como un centro de investigación de referencia internacional neste ámbito, capaz de realizar unha investigación de gran relevancia científica e impacto socioeconómico e cunha alta capacidade de formación e atracción de talento.

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Autonomic indicators of self-transcendence: insights from the numadelic VR paradigm
(Oxford University Press, 2026-04-22) Bonnelle, Valerie; Parola, Giulia; Andreu, Catherine; Hardy, Joseph L.; Wall, Justin; Timmermann, Christopher; Cebolla, Ausiàs; Wrzesien, Maja; Glowacki, David Ryan; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
Self-transcendent experiences (STEs) offer profound and beneficial shifts in perspective, yet remain largely inaccessible outside elite contemplative or pharmacological contexts. Although neural measures have advanced our understanding of these states, their cost and limited ecological validity restrict broader application. This study evaluates heart rate variability (HRV) amplitude, a measure reflecting dynamic sympathovagal engagement, as a cost-effective and sustainable physiological marker of STE during ‘numadelic’ virtual reality (VR) experiences designed to dissolve self-boundaries and foster embodied presence. Building on previous work showing (i) associations between non-ordinary states of consciousness (NOSC) and autonomic activity during psychedelic administration, and (ii) comparable STE intensity in non-drug numadelic VR, we tested whether HRV amplitude reflects STE depth and relates to affective and relational outcomes during numadelic VR. Ninety-six participants engaged in guided meditation either in numadelic VR or a non-VR audio-guided group format. Cardiac and respiratory data were recorded during the session, alongside pre- and post-meditation psychological assessments. Findings show that HRV amplitude measured during numadelic VR correlates with subjective STE ratings, as well as compassion traits, and emotional improvement following practice. Reanalysis of data from a prior psychedelic study further supports the relevance of this measure across different methods of inducing NOSCs. These results advance the psychophysiological mapping of STEs and identify HRV amplitude as a promising real-time biomarker that may help guide participants toward self-transcendent states within adaptive environments. By integrating contemplative science with immersive design, this work contributes to scalable tools that broaden access to and deepen understanding of STEs.
Cell Detection in Biomedical Immunohistochemical Images Using Unsupervised Segmentation and Deep Learning
(MDPI, 2025) Al Tarawneh, Zakaria A.; Tarawneh, Ahmad S.; Mbaidin, Almoutaz; Fernández Delgado, Manuel; Gándara Vila, Pilar; Hassanat, Ahmad; Cernadas García, Eva; Universidade de Santiago de Compostela. Departamento de Cirurxía e Especialidades Médico-Cirúrxicas; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
Accurate computer-aided cell detection in immunohistochemistry images of different tissues is essential for advancing digital pathology and enabling large-scale quantitative analysis. This paper presents a comprehensive comparison of six unsupervised segmentation methods against two supervised deep learning approaches for cell detection in immunohistochemistry images. The unsupervised methods are based on the continuity and similarity image properties, using techniques like clustering, active contours, graph cuts, superpixels, or edge detectors. The supervised techniques include the YOLO deep learning neural network and the U-Net architecture with heatmap-based localization for precise cell detection. All these methods were evaluated using leave-one-image-out cross-validation on the publicly available OIADB dataset, containing 40 oral tissue IHC images with over 40,000 manually annotated cells, assessed using precision, recall, and 𝐹1-score metrics. The U-Net model achieved the highest performance for cell nuclei detection, an 𝐹1-score of 75.3%, followed by YOLO with 𝐹1 = 74.0%, while the unsupervised OralImmunoAnalyser algorithm achieved only 𝐹1 = 46.4%. Although the two former are the best solutions for automatic pathological assessment in clinical environments, the latter could be useful for small research units without big computational resources.
OralImmunoAnalyser: a software tool for immunohistochemical assessment of oral leukoplakia using image segmentation and classification models
(Frontiers Media, 2024-02-26) Abdullah AL Tarawneh, Zakarya; Pena Cristóbal, Maite; Cernadas García, Eva; Suárez Peñaranda, José Manuel; Fernández Delgado, Manuel; Mbaidin, Almoutaz; Gallas Torreira, María Mercedes; Gándara Vila, Pilar; Universidade de Santiago de Compostela. Departamento de Cirurxía e Especialidades Médico-Cirúrxicas; Universidade de Santiago de Compostela. Departamento de Ciencias Forenses, Anatomía Patolóxica, Xinecoloxía e Obstetricia, e Pediatría; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
Oral cancer ranks sixteenth amongst types of cancer by number of deaths. Many oral cancers are developed from potentially malignant disorders such as oral leukoplakia, whose most frequent predictor is the presence of epithelial dysplasia. Immunohistochemical staining using cell proliferation biomarkers such as ki67 is a complementary technique to improve the diagnosis and prognosis of oral leukoplakia. The cell counting of these images was traditionally done manually, which is time-consuming and not very reproducible due to intra- and inter-observer variability. The software presently available is not suitable for this task. This article presents the OralImmunoAnalyser software (registered by the University of Santiago de Compostela–USC), which combines automatic image processing with a friendly graphical user interface that allows investigators to oversee and easily correct the automatically recognized cells before quantification. OralImmunoAnalyser is able to count the number of cells in three staining levels and each epithelial layer. Operating in the daily work of the Odontology Faculty, it registered a sensitivity of 64.4% and specificity of 93% for automatic cell detection, with an accuracy of 79.8% for cell classification. Although expert supervision is needed before quantification, OIA reduces the expert analysis time by 56.5% compared to manual counting, avoiding mistakes because the user can check the cells counted. Hence, the SUS questionnaire reported a mean score of 80.9, which means that the system was perceived from good to excellent. OralImmunoAnalyser is accurate, trustworthy, and easy to use in daily practice in biomedical labs. The software, for Windows and Linux, with the images used in this study, can be downloaded from https://citius.usc.es/transferencia/software/oralimmunoanalyser for research purposes upon acceptance.
Observational cohort study of a group-based VR program to improve mental health and wellbeing in people with life-threatening illnesses
(Frontiers Media, 2025-01-07) Kettner, Hannes; Glowacki, David Ryan; Wall, Justin; Carhart-Harris, Robin L.; Roseman, Leor; Hardy, Joseph L.; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
Introduction: Being diagnosed with a life-threatening illness (LTI) is often accompanied by feelings of fear, uncertainty, and loneliness that can severely impact mental health. Relatively few interventions are available to address the existential concerns of individuals facing LTI, while treatment of the underlying physical ailment typically remains the priority of the healthcare system. Research has shown that psychedelic-assisted psychotherapy (PAT) holds promise for supporting mental health in people with LTIs. However, PAT’s potential in this population remains curtailed by several limitations, including regulatory and accessibility issues. Novel approaches that could provide some of the benefits of psychedelic experiences, while avoiding associated challenges, would therefore be highly desirable for supporting the mental wellbeing of people with LTIs. Among such interventions, virtual reality (VR)-based experiences have been suggested as a promising candidate. We here evaluate a program that includes weakly representational, multi-user VR experiences based on a design aesthetic previously described as “numadelic,” which has been demonstrated to elicit self-transcendent experiences comparable to psychedelics. Methods: A prospective cohort study design was used to assess the effects of “Clear Light” (CL), a group-based, 6-session multimedia program that included VR experiences, video calls, and text chats spanning 3 weeks. Participants were individuals suffering from LTIs that self-selected to participate in the CL program. A total of N = 15 participants were evaluated based on assessments 1 week before and after the program, using self-report measures of anxiety, depression, wellbeing, and secondary psychological outcomes. Results: The intervention was well-tolerated among participants. Significant improvements with moderate effect sizes were observed on self-reported measures of anxiety, depression, and wellbeing. Secondary measures assessing demoralization, connectedness, and spiritual wellbeing also showed significant improvements. Discussion: This observational study demonstrated the feasibility and potential benefits of a group-based VR program that can be delivered at-home to people suffering from LTIs. While conclusions are presently limited by the lack of randomization or a comparison group, our findings strongly suggest further research is warranted, including randomized controlled trials.
Reconstruction of phylogenetic trees via graph-splitting using quantum computing
(Springer, 2026-04-06) Fernández Otero, Nicolás; Fernández Pena, Anselmo Tomás; Pichel Campos, Juan Carlos; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
Quantum computing applies principles of quantum mechanics, such as superposition and entanglement, to process information with exponential parallelism. This paradigm offers significant computational advantages over classical methods, particularly for NP-hard problems like phylogenetic tree reconstruction in evolutionary biology. Phylogenetic trees model the evolutionary relationships among species or genes, and their reconstruction is computationally challenging as the number of possible topologies grows exponentially with the number of taxa. To address this, biologists often rely on heuristic methods; however, recent work has shown that recursive graph-cut techniques can achieve high accuracy in phylogenetic inference, though at high computational cost. In this study, we present a quantum algorithm based on the normalized cut ( ) criterion, enabling efficient recursive graph partitioning. Implemented using Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA), demonstrating promising results on real quantum hardware for complex bioinformatics tasks.
Clever domain adaptation strategies for BERT in the task of hostile-language detection
(Springer, 2026-03-31) Villa Cueva, Emilio; Aragón Saenzpardo, Mario Ezra; López Monroy, Adrián; Sánchez Vega, Fernando; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
Cyberbullying has experienced a surge in recent years, mainly due to the widespread adoption of social media platforms. This trend manifests in multiple ways, with hostile language being one of the most common. The latter underscores the urgent need for robust detection methods to address this issue effectively. To address this problem, we propose a novel pipeline to enhance hostile language detection in social media. Our approach consists of a combination of two ideas: First, we propose conducting a Domain Adaptation procedure to specialize the knowledge of a pre-trained BERT, making it more specialized in the domain of social media. For this adaptation, we modify the traditional random Masked Language Modeling technique and propose three novel strategies for selecting the subset of tokens to mask out cleverly. Second, we tailor an Adversarial Regularizer when fine-tuning the adapted BERT for specific hostile-language datasets. We evaluate the performance of our method for detecting hate speech, aggressiveness, offensiveness, and sexism. Our results show that the Domain Adaptation procedure significantly outperforms vanilla BERT, and the Adversarial Regularizer can lead to more robust fine-tuning, thereby enhancing performance. Moreover, we demonstrate that these methods can be used together to achieve an even more significant performance boost.
Matemáticas aplicadas al procesamiento de imágenes: una perspectiva práctica de enseñanza en niveles postobligatorios no universitarios en Tecnologías de la Información
(Dykinson, 2023) Castiñeira Veiga, Gonzalo; Díaz Parga, César; Universidade de Santiago de Compostela. Departamento de Didácticas Aplicadas; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
Aunque las matemáticas han evolucionado y se han consolidado como una disciplina esencial en la educación, la relación emocional del alumnado con ellas no ha mejorado al mismo ritmo, lo que genera desmotivación y desconexión. Esta situación se asocia con el abandono de itinerarios científicos e incluso con el fracaso escolar, en parte porque muchos estudiantes no perciben su utilidad a corto plazo y la enseñanza se mantiene en un enfoque academicista y propedéutico. Para revertirlo, se proponen metodologías innovadoras que conecten las matemáticas con contextos cercanos y significativos, destacando las propuestas STEAM por integrar distintas áreas del conocimiento y aportar relevancia práctica. El problema se acentúa en etapas preuniversitarias, donde las matemáticas se perciben como un obstáculo; por ello, se plantea aprovechar materias transversales no ligadas directamente a la EBAU, como TIC en 2º de bachillerato, incorporando elementos motivadores como el procesamiento de imágenes y el uso de inteligencia artificial.
Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis
(Elsevier, 2023-01-18) Matabuena, Marcos; Salgado Castro, Francisco Javier; Nieto Fontarigo, Juan José; Álvarez-Puebla, María J.; Arismendi, Ebymar; Barranco, Pilar; Bobolea, Irina; Caballero, María L.; Cañas, José Antonio; Cárdaba, Blanca; Cruz, María Jesús; Curto, Elena; Domínguez-Ortega, Javier; Luna, Juan Alberto; Martínez-Rivera, Carlos; Mullol, Joaquim; Muñoz, Xavier; Rodríguez-García, Javier; Olaguibel, José María; Picado, César; Plaza, Vicente; Quirce, Santiago; Rial, Manuel J.; Romero-Mesones, Christian; Sastre, Beatriz; Soto-Retes, Lorena; Valero, Antonio; Valverde-Monge, Marcela; Pozo, Victoria del; Sastre, Joaquín; González Barcala, Francisco Javier; Universidade de Santiago de Compostela. Departamento de Bioquímica e Bioloxía Molecular; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Universidade de Santiago de Compostela. Departamento de Psiquiatría, Radioloxía, Saúde Pública, Enfermaría e Medicina
Introduction The definition of asthma phenotypes has not been fully established, neither there are cluster studies showing homogeneous results to solidly establish clear phenotypes. The purpose of this study was to develop a classification algorithm based on unsupervised cluster analysis, identifying clusters that represent clinically relevant asthma phenotypes that may share asthma-related outcomes. Methods We performed a multicentre prospective cohort study, including adult patients with asthma (N=512) from the MEGA study (Mechanisms underlying the Genesis and evolution of Asthma). A standardised clinical history was completed for each patient. Cluster analysis was performed using the kernel k-groups algorithm. Results Four clusters were identified. Cluster 1 (31.5% of subjects) includes adult-onset atopic patients with better lung function, lower BMI, good asthma control, low ICS dose, and few exacerbations. Cluster 2 (23.6%) is made of adolescent-onset atopic asthma patients with normal lung function, but low adherence to treatment (59% well-controlled) and smokers (48%). Cluster 3 (17.1%) includes adult-onset patients, mostly severe non-atopic, with overweight, the worse lung function and asthma control, and receiving combination of treatments. Cluster 4 (26.7%) consists of the elderly-onset patients, mostly female, atopic (64%), with high BMI and normal lung function, prevalence of smokers and comorbidities. Conclusion We defined four phenotypes of asthma using unsupervised cluster analysis. These clusters are clinically relevant and differ from each other as regards FEV1, age of onset, age, BMI, atopy, asthma severity, exacerbations, control, social class, smoking and nasal polyps.
AI-based emotion recognition in dementia through facial expression: A scoping review
(Elsevier, 2025-11-27) Gerbaudo González, Noelia; Condori Fernández, Nelly; Catalá Bolós, Alejandro; Gandoy Crego, Manuel; Universidade de Santiago de Compostela. Departamento de Psicoloxía Evolutiva e da Educación; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Universidade de Santiago de Compostela. Departamento de Psiquiatría, Radioloxía, Saúde Pública, Enfermaría e Medicina
Emotion assessment in dementia care is vital for patient well-being and effective care planning. Traditional methods are often subjective and time-consuming. This study examines the use of AI-based facial expression analysis for emotion recognition in dementia patients. A scoping review was conducted using the SPIDER strategy. Five databases—PubMed, Scopus, PsycInfo, ProQuest, and IEEE Xplore —were consulted, with additional records identified through snowballing. Data on participant characteristics, intervention details, non-AI comparisons, and clinical outcomes were categorized. Two authors independently screened records and extracted data on AI driven tools. The review analyzed 11 studies, primarily using deep neural networks. While most studies relied on pre-existing datasets, some collected original data. The studies focused on assessing a variety of emotions, with an emphasis on detecting basic emotions and, in some cases, more complex emotional states. AI applications included early detection, diagnosis, intervention impact assessment, and reliability testing. Comparisons were made with traditional assessment tools. This scoping review highlights the potential of AI tools to improve dementia care. However, standardized data collection and processing protocols are needed to advance AI in emotion recognition for dementia patients. Integrating multiple data sources and addressing dataset limitations are crucial for improving model accuracy and representativeness. Ethical considerations, including privacy and data security, must be prioritized when developing and implementing AI tools in this population. Interdisciplinary collaboration is essential to fully harness their potential
Single-Shot Transient Imaging Via Compressed Time-Of-Flight Imaging And Dictionary Learning
(IEEE, 2026-02-17) Fayyaz Shahandashti, Peyman; López Martínez, Paula; Brea Sánchez, Víctor Manuel; García-Lesta, Manuel; Heredia Conde, Miguel; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
In this work, we propose a single-shot transient imaging framework that combines sparsity-based signal modeling with compressive frequency-domain sampling. The temporal response function of a scene encodes rich information about depth and light transport, and its accurate reconstruction is critical for various imaging applications. To enable transient recovery from a limited number of measurements, we exploit learned sparse representations in an optimized dictionary basis. We compare multiple sampling strategies in the frequency domain and show that both transient profiles and depth maps can be reconstructed under highly compressed acquisition. Notably, we achieve full transient reconstruction using only 16 modulation frequencies, based on real correlation functions, enabling practical singleshot acquisition through spatial frequency multiplexing
Recent Advances in Computational Time-of-Flight Imaging
(IEEE, 2024-04-01) Heredia Conde, Miguel; López Paredes, Álvaro; Ahmed, Faisal; Fayyaz Shahandashti, Peyman; López Martínez, Paula; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
Las cámaras de tiempo de vuelo (ToF) son dispositivos de imagen 3D que capturan la geometría de una escena, aprovechando que el tiempo de viaje de los fotones está directamente relacionado con la distancia recorrida. Este tipo de sensor es muy prometedor en áreas de aplicación emergentes, pero varias deficiencias dificultan su uso. En concreto, nos centramos en el consumo de energía relativamente alto en comparación con las cámaras convencionales, el rango limitado en el que se puede estimar la profundidad con precisión y las distorsiones de medición producidas por el contenido armónico de las formas de onda de modulación/demodulación y la interferencia multitrayecto. En este trabajo, presentamos avances recientes en la computación de imágenes ToF con el objetivo de superar estas limitaciones, permitiendo el funcionamiento pasivo en interiores, el funcionamiento a largo plazo en exteriores y la realización de operaciones de onda continua multifrecuencia de un solo disparo con mínima distorsión armónica. El funcionamiento pasivo se logra aprovechando fuentes de luz modulada, como los módulos LiFi o de Comunicaciones por Luz Visible (VLC). De forma independiente, se pueden obtener ganancias en el rango operativo mediante la conformación de pulsos ultracortos combinada con demodulación codificada de baja densidad. Se logra un muestreo de Fourier preciso con mínima distorsión armónica mediante la inducción de efectos de resonancia personalizados en los píxeles del tiempo de vuelo. Los resultados iniciales de la evaluación demuestran el potencial de estos enfoques computacionales de imágenes 3D para superar las limitaciones mencionadas.
Redundant floating-point decimal CORDIC algorithm
(IEEE, 2012-11) Vázquez Álvarez, Álvaro; Villalba Moreno, Julio; Antelo Suárez, Elisardo; López Zapata, Emilio; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
In this work we propose a new decimal redundant CORDIC algorithm to manage transcendental functions, using floating-point representation. The algorithms determine the direction of the elementary rotation using sign estimations. Unlike binary redundant CORDIC, repetition of iterations are not required to ensure convergence since novel decimal codes have been carefully selected with sufficient redundancy to prevent any repetition. The algorithms are mapped to a low-cost unit based on a decimal 3-2 carry-save adder which can also be used as a floating-point decimal division unit. Compared to current decimal floating-point units, the implementation of our algorithm involves minor modifications of the native hardware, while providing a huge set of elementary functions
Iterative algorithm and architecture for exponential, logarithm, powering, and root extraction
(IEEE, 2013-09) Vázquez Álvarez, Álvaro; Díaz Bruguera, Javier; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
An algorithm and architecture for powering computation and root extraction, with fixed–point and floating–point exponents, is presented in this paper. The algorithm is based on an optimized iterative sequence of parallel and/or overlapped operations: (1) reciprocal, (2) high–radix digit–recurrence logarithm, (3) left–to–right carry–free multiplication and (4) high–radix on–line exponential. A redundant number system is used to allow for the overlapping of the different operations of the algorithm. As the logarithm and exponential are part of the sequence of operations, some minor changes are made to allow for the independent computation of the logarithm and exponential functions. A sequential implementation of the algorithm is proposed and the execution times and hardware requirements are estimated for single and double-precision floating-point computations. These estimates are obtained for several radices, according to an approximate model for the delay and area of the main logic blocks, and help to determine the radix values which lead to the most efficient implementations
Fast radix-10 multiplication using redundant BCD codes
(IEEE, 2014-08) Vázquez Álvarez, Álvaro; Antelo Suárez, Elisardo; Díaz Bruguera, Javier; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
We present the algorithm and architecture of a BCD parallel multiplier that exploits some properties of two different redundant BCD codes to speedup its computation: the redundant BCD excess-3 code (XS-3), and the overloaded BCD representation (ODDS). In addition, new techniques are developed to reduce significantly the latency and area of previous representative high-performance implementations. Partial products are generated in parallel using a signed-digit radix-10 recoding of the BCD multiplier with the digit set [-5, 5], and a set of positive multiplicand multiples (0X, 1X, 2X, 3X, 4X, 5X) coded in XS-3. This encoding has several advantages. First, it is a self-complementing code, so that a negative multiplicand multiple can be obtained by just inverting the bits of the corresponding positive one. Also, the available redundancy allows a fast and simple generation of multiplicand multiples in a carry-free way. Finally, the partial products can be recoded to the ODDS representation by just adding a constant factor into the partial product reduction tree. Since the ODDS uses a similar 4-bit binary encoding as non-redundant BCD, conventional binary VLSI circuit techniques, such as binary carry-save adders and compressor trees, can be adapted efficiently to perform decimal operations. To show the advantages of our architecture, we have synthesized a RTL model for 16×16-digit and 34×34-digit multiplications and performed a comparative survey of the previous most representative designs. We show that the proposed decimal multiplier has an area improvement roughly in the range 20-35 percent for similar target delays with respect to the fastest implementation
Piezoelectric Energy Harvesting Using Fully Integrated SECE Method: An Analytical Study of the Residual Voltage
(IEEE, 2025-12-09) Vicente García, Laura; Pereira Rial, Óscar; López Martínez, Paula; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
This article presents an analytical study of the residual voltage left in the piezoelectric capacitor after each cycle in inductor-less Synchronous Electric Charge Extraction (SECE) methods. A closed-form expression for this voltage is derived, and its impact on the energy harvesting efficiency is examined. The resulting equations are validated using numerical methods and spectre simulations. The analysis highlights key designs constraints, such as the DC-DC converter’s resistance, that limit energy extraction in fully integrated SECE-based harvesters.
CDS Free Frame Differencing Event Vision Pixel with Lateral Overflow Capacitor for Dynamic Range Extension
(IEEE, 2023-12-04) Jaklin, Marko; García Lesta, Daniel; López Martínez, Paula; Brea Sánchez, Víctor Manuel; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
This paper reports on a redesign of an event pixel by frame differencing with a lateral overflow capacitor for dynamic range extension. In the first prototype, the inclusion of an inpixel correlated double sampling (CDS) and an algorithm for dynamic range extension resulted in a large pixel area, with a 32μm pitch in 180 nm CMOS technology. This paper studies a CDS free version of our former pixel through post-layout simulations. The pixel pitch is shrinked to 22μm, power consumption is lowered and speed is preserved.
Maximum Output Power Point Tracking for Low Power Photovoltaic Energy Harvesting Systems
(IEEE, 2023-07-31) Vicente García, Laura; Pereira Rial, Óscar; López Martínez, Paula; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
A novel approach for finding the maximum output power point of a photovoltaic energy harvesting system is proposed in this paper. It is based on maximizing the power delivered to the load taking into account the performance of the whole system in contrast to conventional approaches focused on tracking the maximum power point of the photovoltaic transducer alone. The output power tracking is performed by measuring the output voltage, which significantly simplifies the hardware complexity and consequently reduces the power consumption. A circuit implementation of the entire system is presented and validated using electrical simulations. The designed circuit is self powered and is able to successfully supply a load with voltage levels higher than 1.2V. The system achieves a peak efficiency of 80.51% when the input power is equal to 28.03 μW.
5-Bit Signed SRAM-Based In-Memory Computing Cell
(IEEE, 2024-07-08) Karimpour, Faranak; Pardo, F.; García Lesta, Daniel; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
Hardware accelerators are critical in providing real-time processing for edge computing applications, particu-larly in the context of convolutional neural networks. A crucial challenge in this context is achieving low power consumption while maintaining an appropriate performance in terms of accuracy. This work delves into a thorough analysis of prospective architectures for the core cell of the multiply -and -accumulate function, monitoring each structure's crucial benefits and drawbacks. It includes electrical simulations comparing their performance in a 180 nm process node for 1.8 V and 3.3 V. Moreover, a process corner simulation is proposed to identify on-chip process variations in the voltage error of the proposed design under different input voltages. Notably, the minimum corner errors observed at +15 and -7 sign bits are 0.45 % and 0.63 %, respectively. The significant outcome highlights that the single-switch implementation achieves optimal performance, displaying the lowest error value of 0.14%, specifically at the + 15 sign bit and operating at 1.8 V.
Probing for idiomaticity in vector space models
(Association for Computational Linguistics, 2021-04) García González, Marcos; Vieira, Tiago Kramer; Scarton, Carolina; Idiart, Marco; Villavicencio, Aline; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Universidade de Santiago de Compostela. Departamento de Lingua e Literatura Españolas, Teoría da Literatura e Lingüística Xeral; Merlo, Paola; Tiedemann, Jorg; Tsarfaty, Reut
Contextualised word representation models have been successfully used for capturing different word usages and they may be an attractive alternative for representing idiomaticity in language. In this paper, we propose probing measures to assess if some of the expected linguistic properties of noun compounds, especially those related to idiomatic meanings, and their dependence on context and sensitivity to lexical choice, are readily available in some standard and widely used representations. For that, we constructed the Noun Compound Senses Dataset, which contains noun compounds and their paraphrases, in context neutral and context informative naturalistic sentences, in two languages: English and Portuguese. Results obtained using four types of probing measures with models like ELMo, BERT and some of its variants, indicate that idiomaticity is not yet accurately represented by contextualised models
Live demonstration: a frame-based CMOS vision sensor with high dynamic range for events generation
(IEEE Xplore, 2025-05-25) Jaklin, Marko; García Lesta, Daniel; López Martínez, Paula; Brea Sánchez, Víctor Manuel; Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS); Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
This live demonstration shows a frame-based CMOS vision sensor with high dynamic range for event generation. Our CMOS vision sensor features 64 × 64 processing elements that comprise one 4T-APS and local circuitry to provide events and high dynamic range extension. The event generation is performed synchronously through the threshold of the frame difference between consecutive frames. The dynamic range extension is carried out per-pixel with the overflow capacitance method. The sensor can reach up to thousands of event frames per second. Electrical simulations indicate a dynamic range of 85 dB, which narrows the gap with dynamic vision sensors

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