Publications

Neuromorphic engineering is a growing and promising discipline nowadays. Neuro-inspiration and brain understanding applied to solve engineering problems is boosting new architectures, solutions and products today. The biological brain and neural systems process information at relatively low speeds through small components, called neurons, and it is impressive how they connect each other to construct complex architectures to solve in a quasi-instantaneous way visual and audio processing tasks, object detection and tracking, target approximation, grasping..., etc., with very low power. Neuromorphs are beginning to be very promising for a new era in the development of new sensors, processors, robots and software systems that mimic these biological systems. The event-driven Scorbot (ED-Scorbot) is a robotic arm plus a set of FPGA / microcontroller's boards and a library of FPGA logic joined in a completely event-based framework (spike-based) from the sensors to the actuators. It is located in Seville (University of Seville) and can be used remotely. Spike-based commands, through neuro-inspired motor controllers, can be sent to the robot after visual processing object detection and tracking for grasping or manipulation, after complex visual and audio-visual sensory fusion, or after performing a learning task. Thanks to the cascade FPGA architecture through the Address-Event-Representation (AER) bus, supported by specialized boards, resources for algorithms implementation are not limited.

Early and precise detection of brain tumors is critical for improving clinical outcomes and patient quality of life. This research focused on developing an image classifier using convolutional neural networks (CNN) to detect brain tumors in magnetic resonance imaging (MRI). Brain tumors are a significant cause of morbidity and mortality worldwide, with approximately 300,000 new cases diagnosed annually. Magnetic resonance imaging (MRI) offers excellent spatial resolution and soft tissue contrast, making it indispensable for identifying brain abnormalities. However, accurate interpretation of MRI scans remains challenging, due to human subjectivity and variability in tumor appearance. This study employed CNNs, which have demonstrated exceptional performance in medical image analysis, to address these challenges. Various CNN architectures were implemented and evaluated to optimize brain tumor detection. The best model achieved an accuracy of 97.5%, sensitivity of 99.2%, and binary accuracy of 98.2%, surpassing previous studies. These results underscore the potential of deep learning techniques in clinical applications, significantly enhancing diagnostic accuracy and reliability.

La sociedad avanza, y este avance favorece la aparicion de nuevas necesidades, las cuales son cubiertas por profesionales especializados en campos especificos, como medicos, ingenieros, profesores, etc. Pero cada vez mas, estos nuevos “problemas” requieren soluciones complejas y multidisciplinares, haciendo uso de varios campos de conocimiento. Es por esto que, en los ultimos anos, se hayan creado nuevas titulaciones en el ambito universitario para formar a profesionales que posean los conocimientos necesarios para afrontar estos nuevos retos. Este es el caso del grado en Ingenieria de la Salud, el cual tiene un caracter multidisciplinar, combinando conocimientos biomedicos aplicados a la ingenieria informatica e ingenieria en general. Los alumnos que eligen esta titulacion se pueden catalogar en dos perfiles diferenciados: los procedentes de la rama cientifica-tecnologica y los de ciencias de la salud del bachillerato. Dado que las asignaturas de la titulacion en los primeros anos son de formacion basica y se dividen practicamente en 50% de una rama y 50% de la otra, segun la procedencia del alumno puede que encuentre mayor dificultad en las asignaturas que no son de la suya en concreto. En el curso academico 14/15, propusimos la realizacion de un proyecto en grupo cuyos integrantes estaban combinados entre alumnos de la rama cientifica-tecnologica y de la salud, y cuyo tema requeria conocimientos de ambos campos, de forma que hubiera una colaboracion entre ambas partes. La finalidad de este proyecto era que los alumnos asimilaran los conceptos teoricos que eran nuevos para ellos aplicandolos a una situacion real apoyandose en el resto de los miembros del grupo. El proyecto se desarrollo siguiendo la metodologia de aprendizaje ABP aplicada en las sesiones practicas de la asignatura, marcandose en cada una de estas sesiones una serie de hitos que los alumnos deberan ir completando. Se midio el grado de satisfaccion de los alumnos mediante un cuestionario, obteniendo como resultado un alto grado de satisfaccion.

Falls are one of the most frequent causes of injuries in elderly people.Wearable Fall Detection Systems provided a ubiquitous tool for monitoring and alert when these events happen.Recurrent Neural Networks (RNN) are algorithms that demonstrates a great accuracy in some problems analyzing sequential inputs, such as temporal signal values.However, their computational complexity are an obstacle for the implementation in IoT devices.This work shows a performance analysis of a set of RNN architectures when trained with data obtained using different sampling frequencies.These architectures were trained to detect both fall and fall hazards by using accelerometers and were tested with 10-fold cross validation, using the F1-score metric.The results obtained show that sampling with a frequency of 25Hz does not affect the effectiveness, based on the F1-score, which implies a substantial increase in the performance in terms of computational cost.The architectures with two RNN layers and without a first dense layer had slightly better results than the smallest architectures.In future works, the best architectures obtained will be integrated in an IoT solution to determine the effectiveness empirically.

Background: Monkeypox is similar to classical smallpox and, until now, isolated cases have been detected in Africa, but in 2022 it has spread around the world and was declared an international health emergency by the WHO at the end of July.Objective: In this work, we collect monkeypox images from official government websites and public datasets (as well as images of healthy people and people with other skin diseases) and create the MonkeypoxSkin dataset (publicly available), which contains close-up images of the skin where you can see the posthulas formed by the disease. With these images, we design, implement, and evaluate several diagnostic aid systems for monkeypox disease.Methods: The classifiers developed and evaluated are based on Convolutional Neural Networks models and some ensembles composed of a combination of those models, obtaining automatic classification results between healthy, monkeypox and other skin damages, given a close skin tissue image.Results: The results show a system accuracy greater than 93% when using a unique CNN model (VGG-19 and ResNet50), and greater than 98% when using a CNN ensemble formed by ResNet50, EfficientNet-B0 and MobileNet-V2.Conclusions: In contrast to the work found to date, this classifier focusses on disease classification based on a low-resolution image obtained from the skin surface, so it can be integrated into a mobile application for mass screening. To do this, it has been necessary to design a proprietary dataset that is publicly available for use. Furthermore, the results obtained improve the accuracy of the previous works.

Neural networks algorithms are commonly used to recognize patterns from different data sources such as audio or vision. In image recognition, Convolutional Neural Networks are one of the most effective techniques due to the high accuracy they achieve. This kind of algorithms require billions of addition and multiplication operations over all pixels of an image. However, it is possible to reduce the number of operations using other computer vision techniques rather than frame-based ones, e.g. neuromorphic frame-free techniques. There exists many neuromorphic vision sensors that detect pixels that have changed their luminosity. In this study, an event-based convolution engine for FPGA is presented. This engine models an array of leaky integrate and fire neurons. It is able to apply different kernel sizes, from 1x1 to 7x7, which are computed row by row, with a maximum number of 64 different convolution kernels. The design presented is able to process 64 feature maps of 7x7 with a latency of 8.98μs.

Colorectal cancer is the second leading cause of cancer-related deaths worldwide. To prevent deaths, regular screenings with histopathological analysis of colorectal tissue should be performed. A diagnostic aid system could reduce the time required by medical professionals, and provide an initial approach to the final diagnosis. In this study, we analyze low computational custom architectures, based on Convolutional Neural Networks, which can serve as high-accuracy binary classifiers for colorectal cancer screening using histopathological images. For this purpose, we carry out an optimization process to obtain the best performance model in terms of effectiveness as a classifier and computational cost by reducing the number of parameters. Subsequently, we compare the results obtained with previous work in the same field. Cross-validation reveals a high robustness of the models as classifiers, yielding superior accuracy outcomes of 99.4 ± 0.58% and 93.2 ± 1.46% for the lighter model. The classifiers achieved an accuracy exceeding 99% on the test subset using low-resolution images and a significantly reduced layer count, with images sized at 11% of those used in previous studies. Consequently, we estimate a projected reduction of up to 50% in computational costs compared to the most lightweight model proposed in the existing literature.

Several studies have focused on classifying behavioral patterns in wildlife and captive species to monitor their activities and so to understanding the interactions of animals and control their welfare, for biological research or commercial purposes. The use of pattern recognition techniques, statistical methods and Overall Dynamic Body Acceleration (ODBA) are well known for animal behavior recognition tasks. The reconfigurability and scalability of these methods are not trivial, since a new study has to be done when changing any of the configuration parameters. In recent years, the use of Artificial Neural Networks (ANN) has increased for this purpose due to the fact that they can be easily adapted when new animals or patterns are required. In this context, a comparative study between a theoretical research is presented, where statistical and spectral analyses were performed and an embedded implementation of an ANN on a smart collar device was placed on semi-wild animals. This system is part of a project whose main aim is to monitor wildlife in real time using a wireless sensor network infrastructure. Different classifiers were tested and compared for three different horse gaits. Experimental results in a real time scenario achieved an accuracy of up to 90.7%, proving the efficiency of the embedded ANN implementation.