Electrónica e Computación

Permanent URI for this collectionhttps://hdl.handle.net/10347/34364

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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.
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.
Multi-criteria evaluation and multi-method analysis for appropriately selecting renewable energy sources in Colombia
(Elsevier, 2025-02-27) Moreno Rocha, Christian Manuel; Núñez-Álvarez, José R.; Rivera-Alvarado, Juan; Ghisayz Ruiz, Alfredo; Buelvas-Sánchez, Enderson A.; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
This research explores the implementation of renewable energy technologies for power generation using multi-criteria decision-making (MCDM) methods, including AHP, FAHP, TOPSIS, and FUZZY-TOPSIS. Ten renewable energy alternatives were evaluated across seven geographic regions in Colombia, revealing variability in preferences depending on the method and scenario. Alternatives 6 and 4 frequently stood out, while others showed varied rankings. This study significantly contributes to the energy sector by offering a rigorous framework for selecting renewable generation technologies, supporting sustainable energy planning, and providing a model for replication in global contexts, some key points are: • The study applied systematic MCDM approaches to assess renewable energy sources. • Results demonstrated method-dependent variability and highlighted regional preferences. • It sets a benchmark for integrating sustainable practices into energy planning worldwide.
Data set for the generation of strategies in the selection of renewable energies in Colombia: Evaluation of AHP and FAHP from regional potentials towards a sustainable future
(Elsevier, 2025) Moreno Rocha, Christian Manuel; Arenas Buelvas, Daina; Vega Machado, Itzjak; Pacheco Peña, Juan; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
This analysis examines the evaluation of alternatives using the AHP (Analytic Hierarchy Process) and FAHP (Fuzzy Analytic Hierarchy Process) methodologies in five scenarios (SC1 to SC5), with the aim of comparing the effectiveness of both approaches in incorporating environmental and technical criteria. The findings reveal that, in the SC1 scenario, AHP assigns weights of 14.35 % to A1 and 16.22 % to A2, while FAHP shows a greater dispersion and highlights A6 with 35.22 %. In SC2, AHP favors A1 with 14.16 % and FAHP increases the weight of the environmental criterion to 21.18 %. In SC3, A1 remains the preferred option in both methodologies, although AHP and FAHP exhibit a close weight of 34.00 % and 32.98 %, respectively. In SC4, AHP and FAHP maintain a similar trend, with A1 standing out with 11.12 % and A4 with 34.87 %. Finally, in SC5, the findings show that AHP allocates 8.52 % to A1, while FAHP indicates 10.73 %. The observations suggest that FAHP allows a greater sensitivity to variations in the sub-criteria, thus facilitating a more accurate assessment aligned with sustainability objectives. The relevance of environmental and social criteria across the different scenarios highlights the need to integrate more sustainable approaches into decision-making. It is deduced that, although AHP provides consistent outcomes, FAHP might be more suitable for evaluating alternatives in contexts where complexity and uncertainty are significant. It is recommended to perform sensitivity analysis to delve into the influence of variations in the weights of the criteria on final decisions.
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.
Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines
(SAGE Publications, 2025) Iglesias, Enrique; Vidal, María-Esther; Collarana, Diego; Chaves Fraga, David; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
The significant increase in data volume in recent years has prompted the adoption of knowledge graphs as valuable data structures for integrating diverse data and metadata. However, this surge in data availability has brought to light challenges related to standardization, interoperability, and data quality. Knowledge graph creation faces complexities from large data volumes, data heterogeneity, and high duplicate rates. This work addresses these challenges and proposes data management techniques to scale up the creation of knowledge graphs specified using the RDF Mapping Language (RML). These techniques are integrated into SDM-RDFizer, transforming it into a two-fold solution designed to address the complexities of generating knowledge graphs. Firstly, we introduce a reordering approach for RML triples maps, prioritizing the evaluation of the most selective maps first to reduce memory usage. Secondly, we employ an RDF compression strategy, along with optimized data structures and novel operators, to prevent the generation of duplicate RDF triples and optimize the execution of RML operators. We assess the performance of SDM-RDFizer through established benchmarks. The evaluation showcases the effectiveness of SDM-RDFizer compared to state-of-the-art RML engines, emphasizing the benefits of our techniques. Furthermore, the paper presents real-world projects where SDM-RDFizer has been utilized, providing insights into the advantages of declaratively defining knowledge graphs and efficiently executing these specifications using this engine.
Exploring the role of project status information in effective code smell detection
(Springer, 2024-10-22) Alkharabsheh, Khalid; Alawadi, Sadi; Crespo, Yania; Taboada González, José Ángel; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
Repairing code smells detected in the code or design of the system is one of the activities contributing to increasing the software quality. In this study, we investigate the impact of non-numerical information of software, such as project status information combined with machine learning techniques, on improving code smell detection. For this purpose, we constructed a dataset consisting of 22 systems with various project statuses, 12,040 classes, and 18 features that included 1935 large classes. A set of experiments was conducted with ten different machine learning techniques by dividing the dataset into training, validation, and testing sets to detect the large class code smell. Feature selection and data balancing techniques have been applied. The classifier’s performance was evaluated using six indicators: precision, recall, F-measure, MCC, ROC area, and Kappa tests. The preliminary experimental results reveal that feature selection and data balancing have poor influence on the accuracy of machine learning classifiers. Moreover, they vary their behavior when utilized in sets with different values for the selected project status information of their classes. The average value of classifiers performance when fed with status information is better than without. The Random Forest achieved the best behavior according to all performance indicators (100%) with status information, while AdaBoostM1 and SMO achieved the worst in most of them (> 86%). According to the findings of this study, providing machine learning techniques with project status information about the classes to be analyzed can improve the results of large class detection.
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
Multi-operand decimal addition by efficient reuse of a binary carry-save adder tree
(IEEE, 2010-11) Vázquez Álvarez, Álvaro; Antelo Suárez, Elisardo; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
We present a novel method for hardware design of combined binary/decimal multi-operand adders. More specifically, we apply this method to architectures based on binary CSA (carry-save adder) trees, which are of interest for VLSI implementation of high performance multipliers and other low latency arithmetic units. A remarkable feature of the proposed method is that it allows the reuse of any binary CSA for computing the sum of BCD operands. Decimal corrections are performed in parallel, separately from the computation of the binary sum, such that the layout of the binary carry-save adder does not require any further rearrangement. As a result, the latency of the binary operation is unaffected by the incorporation of hardware support for decimal, while the latency for the decimal mode is close to the latency figures of dedicated decimal multi-operand adders. We show that our combined architecture is competitive in terms of area and delay with respect to other representative proposals, and that it has a more regular layout when implemented in a submicron VLSI technology.
WIP: Unflipping the Classroom: Analyzing the Consequences of Toning Down Blended Learning
(IEEE, 2024) Liz Domínguez, Martín; Caeiro-Rodríguez, Manuel; Llamas Nistal, Martín; Mikic-Fonte, Fernando; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
This research WIP paper describes the case of a first-year engineering course which, contrary to modern trends in education, reduced the presence of blended learning in its methodology, transitioning from the flipped classroom and intensive continuous assessment models back to traditional instruction. Typically, blended learning methodologies like the flipped classroom are regarded to have the positive effects of improving engagement and fostering a student-centered learning approach. However, there are also concerns regarding the potentially increased workload that this system imposes on students. This study analyzes course resource usage trends by students, comparing the final year under the blended learning method with the first year after the switch to traditional instruction. More specifically, the volume of student activity, its timing throughout the semester, and the preferred types of resources are compared between both methodologies. The study found that, in terms of time spent interacting with resources, there was not a significant decrease between both iterations of the course, although the types of resources favored by students do vary between methodologies. Additionally, the activity distribution throughout the semester is observed to be heavily influenced by the timing of exams in both scenarios
Exploring the limits of foundation models in medical image segmentation: a case study with SAM and genetic algorithms
(UNIR, 2026-02-24) Gutiérrez, Juan D.; Lozano García, Nuria; Delgado Muñoz, Emilio; Rubio Largo, Álvaro; Rodríguez Echeverría, Roberto; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
This paper investigates the limits of foundation models in medical image segmentation, mainly focusing on SAM by Meta. While previous research demonstrated SAM’s potential for cost-efficient segmentation, this study explores its performance enhancement through integration with prompt enhancement optimization and genetic algorithms, aiming to minimize user input further. As a proof of concept, we apply this novel approach to lung segmentation tasks using public axial lung CT scans, frontal chest X-ray datasets, and spleen MRIs. Our findings reveal that the genetic algorithm optimization significantly improves SAM’s segmentation accuracy, bringing it closer to the state-of-the-art performance achieved by specifically trained models. In particular, when compared with our previous approach, this technique reaches a 94.85 % Jaccard Index (+3.77 delta) and a 97.17 % Dice Score (+2.50 delta) for lung CT scans, a 93.39 % Jaccard Index (+5.95 delta) and a 96.57 %Dice Score (+3.38 delta) for chest X-rays, and a 91.00 % Jaccard Index (+6.51 delta) and a 95.07 % Dice Score (+4.12 delta) for spleen MRIs. Notably, this improvement is achieved without retraining or modifying SAM’s architecture. However, our analysis also identifies an inherent limitation in this optimization approach, revealing a performance ceiling that cannot be surpassed despite further genetic algorithm iterations. The implications of these findings emphasize the potential of combining foundation models with non-intrusive optimization techniques for cost-effective and accessible medical image segmentation. While dataset-related limitations may affect generalizability, validating the approach across broader clinical scenarios remains essential. Future work should explore applications to additional organs, diverse datasets, and the integration of expert-in-the-loop strategies to enhance clinical utility
Implementation of the exponential function in a floating- point unit
(Springer Nature, 2003-01-01) Vázquez Álvarez, Álvaro; Antelo Suárez, Elisardo; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
In this work we present an implementation of the exponential function in double precision, in a unit that supports IEEE floating-point arithmetic. As existing proposals, the implementation is based on the use of a floating-point multiplier and additional hardware. We decompose the computation into three subexponentials. The first and third subexponentials are computed in a conventional way (table look-up and polynomial approximation). The second subexponential is computed based on a transformation of the slow radix-2 digit-recurrence algorithm into a fast computation by using the multiplier and additional hardware. We present a design process that permits the selection of the most convenient trade-off between hardware complexity and latency. We discuss the algorithm, the implementation, and perform a rough comparison with three proposed designs. Our estimations indicate that the implementation proposed in this work presents better trade-off between hardware complexity and latency than the compared designs
Improved design of high-performance parallel decimal multipliers
(IEEE, 2010-05-01) Vázquez Álvarez, Álvaro; Antelo Suárez, Elisardo; Montuschi, Paolo; Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
The new generation of high-performance decimal floating-point units (DFUs) is demanding efficient implementations of parallel decimal multipliers. In this paper, we describe the architectures of two parallel decimal multipliers. The parallel generation of partial products is performed using signed-digit radix-10 or radix-5 recodings of the multiplier and a simplified set of multiplicand multiples. The reduction of partial products is implemented in a tree structure based on a decimal multioperand carry-save addition algorithm that uses unconventional (non BCD) decimal-coded number systems. We further detail these techniques and present the new improvements to reduce the latency of the previous designs, which include: optimized digit recoders for the generation of 2n-tuples (and 5-tuples), decimal carry-save adders (CSAs) combining different decimal-coded operands, and carry-free adders implemented by special designed bit counters. Moreover, we detail a design methodology that combines all these techniques to obtain efficient reduction trees with different area and delay trade-offs for any number of partial products generated. Evaluation results for 16-digit operands show that the proposed architectures have interesting area-delay figures compared to conventional Booth radix-4 and radix--8 parallel binary multipliers and outperform the figures of previous alternatives for decimal multiplication
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.

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