Persona: Castillo Cara, José Manuel
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Castillo Cara
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José Manuel
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Publicación Ray: Smart indoor/outdoor routes for the blind using Bluetooth 4.0 BLE(Elsevier, 2016) Huaranga Junco, Edgar; Mondragón Ruiz, Giovanny; Salazar, Andree; Orozco Barbosa, Luis; Arias Antúnez, Enrique; Castillo Cara, José ManuelThis work describes the implementation of a cost-effective assistive mobile application aiming to improve the quality of life of visually impaired people. Taking into account the architectural adaptations being done in many cities around the world, such as tactile sidewalks, the mobile application provides support to guide the visually impaired through outdoor/indoor spaces making use of various navigation technologies. The actual development of the application presented herein has been done taking into account that the safety of the end user will very much depend on the robustness, accuracy and timeliness of the information to be provided. Furthermore, we have based our development on open source code: a must for applications to be adapted to the cultural and social characteristics of urban areas across the world.Publicación Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms(SAGE, 2016-08-24) Martínez Gómez, Jesús; Martínez del Horno, Miguel; Brea Luján, Victor Manuel; Orozco Barbosa, Luis; García Varea, Ismael; Castillo Cara, José ManuelThe accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focusing on the design of mechanisms making use of the receiver signal strength indications generated by WLAN (wireless local area network) devices. However, the accuracy and robustness of such mechanisms can be severely compromised due to the random and unpredictable nature of radio channels. In this article, we develop a methodology incorporating various algorithms capable of coping with the unpredictable nature of radio channels. Following a holistic approach, we start by identifying the wireless equipment parameter setting, better meeting the implementation requirements of a robust indoor localization mechanism. We then make use of RANdom SAmple Consensus paradigm: a robust model-fitting mechanism capable of smoothing the data captured during the space survey. Using an experimental setup, we evaluate the benefits of integrating the floor plan and an ordinary Kriging interpolation algorithm in the estimation process. Our main findings show that our proposal can greatly improve the quality of the information to be used in the development of particle-filter-based indoor localization mechanisms.Publicación FROG: A Robust and Green Wireless Sensor Node for Fog Computing Platforms(Hindawi, 2018-04-12) Huaranga Junco, Edgar; Quispe Montesinos, Milner; Orozco Barbosa, Luis; Arias Antúnez, Enrique; Castillo Cara, José ManuelOver the past few years, we have witnessed the widespread deployment of wireless sensor networks and distributed data management facilities: two main building blocks of the Internet of things (IoT) technology. Due to the spectacular increase on the demand for novel information services, the IoT-based infrastructures are more and more characterized by their geographical sparsity and increasing demands giving rise to the need of moving from a cloud to a fog model: a novel deployment paradigm characterized by the provisioning of elastic resources geographically located as close as possible to the end user. Despite the large number of wireless sensor networks already available in the market, there are still many issues to be addressed on the design and deployment of robust network platforms capable of meeting the demand and quality of fog-based systems. In this paper, we undertake the design and development of a wireless sensor node for fog computing platforms addressing two of the main issues towards the development and deployment of robust communication services, namely, energy consumption and network resilience provisioning. Our design is guided by examining the relevant macroarchitecture features and operational constraints to be faced by the network platform. We based our solution on the integration of network hardware platforms already available on the market supplemented by smart power management and network resilience mechanismsPublicación 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 An Analysis of Multiple Criteria and Setups for Bluetooth Smartphone-Based Indoor Localization Mechanism(Hindawi, 2017-10-23) Lovón Melgarejo, Jesús; Bravo Rocca, Gusseppe; Orozco Barbosa, Luis; García Varea, Ismael; Castillo Cara, José ManuelBluetooth Low Energy (BLE) 4.0 beacons will play a major role in the deployment of energy-efficient indoor localization mechanisms. Since BLE4.0 is highly sensitive to fast fading impairments, numerous ongoing studies are currently exploring the use of supervised learning algorithm as an alternative approach to exploit the information provided by the indoor radio maps. Despite the large number of results reported in the literature, there are still many open issues on the performance evaluation of such approach. In this paper, we start by identifying, in a simple setup, the main system parameters to be taken into account on the design of BLE4.0 beacons-based indoor localization mechanisms. In order to shed some light on the evaluation process using supervised learning algorithm, we carry out an in-depth experimental evaluation in terms of the mean localization error, local prediction accuracy, and global prediction accuracy. Based on our results, we argue that, in order to fully assess the capabilities of supervised learning algorithms, it is necessary to include all the three metrics.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.Publicación Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms(Institute of Electrical and Electronics Engineers, 2019-02-15) Lovón Melgarejo, Jesús; Huarcaya Canal, Oscar; Orozco Barbosa, Luis; García Varea, Ismael; Castillo Cara, José ManuelThe development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.