Persona: Castillo Cara, José Manuel
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Castillo Cara
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José Manuel
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Publicación Generative Adversarial Networks for text-to-face synthesis & generation: A quantitative–qualitative analysis of Natural Language Processing encoders for Spanish(Elsevier, 2024-01) Yauri Lozano, Eduardo; Orozco Barbosa, Luis; García Castro, Raúl; Castillo Cara, José ManuelIn recent years, the development of Natural Language Processing (NLP) text-to-face encoders and Generative Adversarial Networks (GANs) has enabled the synthesis and generation of facial images from textual description. However, most encoders have been developed for the English language. This work presents the first study of three text-to-face encoders, namely, the RoBERTa pre-trained model and the Sent2Vec and RoBERTa models, trained with the CelebA dataset in Spanish. It then introduces customised and fine-tuned conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) trained with the CelebA dataset for text-to-face generation in Spanish. To validate the results obtained, a qualitative evaluation was carried out with a visual analysis and a quantitative evaluation based on the IS, FID and LPIPS metrics. Our findings show promising results with respect to the literature, improving the numerical metrics of FID and LPIPS by 5% and 37%, respectively. Our results also show, through a quantitative–qualitative comparison of the cDCGAN training epochs, that the IS metric is not a reliable objective metric to be considered in the evaluation of similar worksPublicación TINTO: Converting Tidy Data into image for classification with 2-Dimensional Convolutional Neural Networks(Elsevier, 2023) Talla Chumpitaz, Reewos; García Castro, Raúl; Orozco Barbosa, Luis; Castillo Cara, José ManuelThe growing interest in the use of algorithms-based machine learning for predictive tasks has generated a large and diverse development of algorithms. However, it is widely known that not all of these algorithms are adapted to efficient solutions in certain tidy data format datasets. For this reason, novel techniques are currently being developed to convert tidy data into images with the aim of using Convolutional Neural Networks (CNNs). TINTO offers the opportunity to convert tidy data into images through the representation of characteristic pixels by implementing two dimensional reduction algorithms: Principal Component Analysis (PCA) and t-distributed Stochastic Neighbour Embedding (t-SNE). Our proposal also includes a blurring technique, which adds more ordered information to the image and can improve the classification task in CNNs.Publicación On the relevance of the metadata used in the semantic segmentation of indoor image spaces(Elsevier, 2021) Vasquez Espinoza, Luis; Orozco Barbosa, Luis; Castillo Cara, José ManuelThe study of artificial learning processes in the area of computer vision context has mainly focused on achieving a fixed output target rather than on identifying the underlying processes as a means to develop solutions capable of performing as good as or better than the human brain. This work reviews the well-known segmentation efforts in computer vision. However, our primary focus is on the quantitative evaluation of the amount of contextual information provided to the neural network. In particular, the information used to mimic the tacit information that a human is capable of using, like a sense of unambiguous order and the capability of improving its estimation by complementing already learned information. Our results show that, after a set of pre and post-processing methods applied to both the training data and the neural network architecture, the predictions made were drastically closer to the expected output in comparison to the cases where no contextual additions were provided. Our results provide evidence that learning systems strongly rely on contextual information for the identification task process.Publicación A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation(Elsevier, 2022-10-17) Talla Chumpitaz, Reewos; Orozco Barbosa, Luis; García Castro, Raúl; Castillo Cara, José ManuelThe growing interest in the use of IoT technologies has generated the development of numerous and diverse applications. Many of the services provided by the applications are based on knowledge of the localisation and profile of the end user. Thus, the present work aims to develop a system for indoor localisation prediction using Bluetooth-based fingerprinting using Convolutional Neural Networks (CNN). For this purpose, a novel technique was developed that simulates the diffusion behaviour of the wireless signal by transforming tidy data into images. For this transformation, we implemented the technique used in painting known as blurring, simulating the diffusion of the signal spectrum. Our proposal also includes the use and a comparative analysis of two dimensional reduction algorithms, PCA and t -SNE. Finally, an evolutionary algorithm was implemented to configure and optimise our solution with the combination of different transmission power levels. The results reported in this work present an accuracy of close to 94%, which clearly shows the great potential of this novel technique in the development of more accurate indoor localisation systems .Publicación BeeGOns!: A Wireless Sensor Node for Fog Computing in Smart City Applications(Institute of Electrical and Electronics Engineers, 2024-01) Vera Panez, Michael; Cuadros Claro, Kewin; Orozco Barbosa, Luis; Castillo Cara, José ManuelThe widespread deployment of sensors interconnected by wireless links and the management and exploitation of the data collected have given rise to the Internet of Things (IoT) concept. In this article, we undertake the design and implementation of a wireless multisensor platform following the fog computing paradigm. Our main contributions are the integration of various off-the-shelf sensors smartly packaged into an air-flow module and the evaluation of the communications services offered implemented on top of two low-power radio communications technologies. Our study is complemented by evaluating the communi- cations services over a wired link. Our results show the superiority of LoRaWAN over ZigBee in terms of power consumption despite its slightly higher computational requirements and an estimation of the gap between the resource usage of the wired link and the two wireless radio technologies.