https://hdl.handle.net/11000/418 Wed, 08 Apr 2026 10:44:25 GMT 2026-04-08T10:44:25Z https://hdl.handle.net/11000/38844 Título : Study of Qu-Based Resonant Microwave Sensors and Design of 3-D-Printed Devices Dedicated to Glucose Monitoring Autor : Juan, Carlos G.; Potelon, Benjamin; Quendo, Cédric; García-Martínez, Héctor; Ávila-Navarro, Ernesto; Bronchalo, Enrique; Sabater-Navarro, José María Resumen : A low-cost, additively manufactured, biocompatible glucose sensor based on the changes in the unloaded quality factor (Qu) with a single split-ring resonator is presented. An exposition of the fundamentals for the use of the Qu as sensing parameter with microwave planar resonant sensors is shown. The convenience of this sensing parameter is analyzed from the theoretical point of view, and practical design and optimization guidelines are inferred with a special focus on the optimization of Qu sensitivity to glucose concentration. For practical demonstration and experimental assessment, a novel inverted microstrip configuration is considered, built upon a customized structure made with a certified biocompatible material thanks to 3-D printing techniques, which is aimed to provide for a stronger interaction between the electromagnetic fields and the sample. Two metallization solutions are investigated, yielding devices operating at 4.50 and 4.62 GHz, with operating Qu of 16.36 and 22.00, relative Qu sensitivities of 1.377 and 2.727, Qu sensitivities to glucose content within the physiological range of 0.3 × 10−3 per mg/dL and 0.6 × 10−3 per mg/dL, sensing areas of approximately 11.7 × 8.8 mm2 and total structure sizes of 950.0 × 35.0 × 3.5 mm3. The devices show good performance with water–glucose solutions covering a wide range of concentrations, involving physiological as well as industry-related ones. Notas: Los derechos de autor de este artículo pertenecen al IEEE. El artículo se encuentra publicado en: https://ieeexplore.ieee.org/abstract/document/9585096 Mon, 12 Jan 2026 11:35:37 GMT https://hdl.handle.net/11000/38844 2026-01-12T11:35:37Z https://hdl.handle.net/11000/38843 Título : Neural Tract Avoidance Path-Planning Optimization: Robotic Neurosurgery Autor : Manrique-Cordoba, Juliana; Martorell, Carlos; Romero-Ante, Juan D.; Sabater-Navarro, José María Resumen : Background: We propose a three-dimensional path-planning method to generate optimized surgical trajectories for steering flexible needles along curved paths while avoiding critical tracts in the context of surgical glioma resection. Methods: Our approach is based on an application of the rapidly exploring random tree algorithm for multi-trajectory generation and optimization, with a cost function that evaluates different entry points and uses the information of MRI images as segmented binary maps to compute a safety trajectory. As a novelty, an avoidance module of the critical neuronal tracts defined by the neurosurgeon is included in the optimization process. The proposed strategy was simulated in real-case 3D environments to reach a glioma and bypass the tracts of the forceps minor from the corpus callosum. Results: A formalism is presented that allows for the evaluation of different entry points and trajectories and the avoidance of selected critical tracts for the definition of new neurosurgical approaches. This methodology can be used for different clinical cases, allowing the constraints to be extended to the trajectory generator. We present a clinical case of glioma at the base of the skull and access it from the upper area while avoiding the minor forceps tracts. Conclusions: This path-planning method offers alternative curved paths with which to reach targets using flexible tools. The method potentially leads to safer paths, as it permits the definition of groups of critical tracts to be avoided and the use of segmented binary maps from the MRI images to generate new surgical approaches. Mon, 12 Jan 2026 11:29:50 GMT https://hdl.handle.net/11000/38843 2026-01-12T11:29:50Z https://hdl.handle.net/11000/38842 Título : N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning Autor : Manrique-Cordoba, Juliana; Casa-Lillo, Miguel Ángel de la; Sabater-Navarro, José María Resumen : This paper presents an n-dimensional reduction algorithm for Learning from Demonstration (LfD) for robotic path planning, addressing the complexity of highdimensional data. The method extends the Douglas–Peucker algorithm by incorporating velocity and orientation alongside position, enabling more precise trajectory simplification. A magnitude-based normalization process preserves proportional relationships across dimensions, and the reduced dataset is used to train Hidden Markov Models (HMMs), where continuous trajectories are discretized into identifier sequences. The algorithm is evaluated in 2D and 3D environments with datasets combining position and velocity. The results show that incorporating additional dimensions significantly enhances trajectory simplification while preserving key information. Additionally, the study highlights the importance of selecting appropriate encoding parameters to achieve optimal resolution. The HMM-based models generated new trajectories that retained the patterns of the original demonstrations, demonstrating the algorithm’s capacity to generalize learned behaviors for trajectory learning in high-dimensional spaces. Mon, 12 Jan 2026 11:28:13 GMT https://hdl.handle.net/11000/38842 2026-01-12T11:28:13Z https://hdl.handle.net/11000/36844 Título : Methods for the Segmentation of Reticular Structures Using çD LiDAR Data: A Comparative Evaluation Autor : Soler Mora, Francisco J.; Peidró Vidal, Adrián; Fabregat-Jaén, Marcos; Payá Castelló, Luis; Reinoso García, Oscar Resumen : Reticular structures are the basis of major infrastructure projects, including bridges, electrical pylons and airports. However, inspecting and maintaining these structures is both expensive and hazardous, traditionally requiring human involvement.While some research has been conducted in this eld of study,most e orts focus on faults identi cation through images or the design of robotic platforms, o en neglecting the autonomous navigation of robots through the structure. is study addresses this limitation by proposing methods to detect navigable surfaces in truss structures, thereby enhancing the autonomous capabilities of climbing robots to navigate through these environments. e paper proposesmultiple approaches for the binary segmentation between navigable surfaces and background from çD point clouds captured frommetallic trusses. Approaches can be classi ed into two paradigms: analytical algorithms and deep learning methods. Within the analytical approach, an ad hoc algorithm is developed for segmenting the structures, leveraging di erent techniques to evaluate the eigendecomposition of planar patches within the point cloud. In parallel, widely used and advanced deep learning models, including PointNet, PointNet++, MinkUNetç¥C, and PointTransformerVç, are trained and evaluated for the same task. A comparative analysis of these paradigms reveals some key insights. e analytical algorithm demonstrates easier parameter adjustment and comparable performance to that of the deep learning models, despite the latter’s higher computational demands. Nevertheless, the deep learning models stand out in segmentation accuracy, with PointTransformerVç achieving impressive results, such as a Mean Intersection Over Union (mIoU) of approximately ÀÞ%. is study highlights the potential of analytical and deep learning approaches to improve the autonomous navigation of climbing robots in complex truss structures. e ndings underscore the trade-o s between computational e ciency and segmentation performance, o ering valuable insights for future research and practical applications in autonomous infrastructure maintenance and inspection. Fri, 11 Jul 2025 11:59:17 GMT https://hdl.handle.net/11000/36844 2025-07-11T11:59:17Z https://hdl.handle.net/11000/36843 Título : Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis Autor : Heredia-Aguado, Enrique; Cabrera, Juan José; Jiménez, Luis Miguel; Valiente, David; Gil, Arturo Resumen : This paper presents a comparison of different image fusion methods for matching visible-spectrum images with thermal-spectrum (far-infrared) images, aimed at enhancing person detection using convolutional neural networks (CNNs). While object detection with RGB images is a well-developed area, it is still greatly limited by lighting conditions. This limitation poses a significant challenge in image detection playing a larger role in everyday technology, where illumination cannot always be controlled. Far-infrared images (which are partially invariant to lighting conditions) can serve as a valuable complement to RGB images in environments where illumination cannot be controlled and robust object detection is needed. In this work, various early and middle fusion techniques are presented and compared using different multispectral datasets, with the aim of addressing these limitations and improving detection performance. Fri, 11 Jul 2025 11:57:12 GMT https://hdl.handle.net/11000/36843 2025-07-11T11:57:12Z https://hdl.handle.net/11000/36842 Título : Detection of UAVs on a collision course using optical flow Autor : Cabrera, Juan José; Gil, Arturo; Payá, Luis; Santo, Antonio; Reinoso, Oscar; Rodríguez, David Resumen : This paper presents a method to detect, track and predict a potential collision with UAVs using an aircraft equipped with a single camera. The method analyses the movement in the camera’s image plane by means of sparse optical flow. In this way, the camera’s own movement can be modelled and cancelled by estimating a homography matrix from a set of corresponding points. Once the movement caused by the camera is cancelled other moving objects can be isolated and the presence of other UAVs can be detected. Additionally, the method predicts potential collisions by examining the alignment between the position and velocity vectors of the UAV, which are estimated up to a scale factor. The proposed method is effective at detecting and predicting collisions with UAVs, regardless of their appearance, size, or movement, making it useful for applications related to airspace security. Fri, 11 Jul 2025 11:56:26 GMT https://hdl.handle.net/11000/36842 2025-07-11T11:56:26Z https://hdl.handle.net/11000/36841 Título : Triplet Neural Networks for the Visual Localization of Mobile Robots Autor : Alfaro, Marcos; Cabrera, Juan José; Jiménez, Luis Miguel; Reinoso, Óscar; Payá, Luis Resumen : Triplet networks are composed of three identical convolutional neural networks that function in parallel and share their weights. These architectures receive three inputs simultaneously and provide three different outputs, and have demonstrated to have a great potential to tackle visual localization. Therefore, this paper presents an exhaustive study of the main factors that influence the training of a triplet network, which are the choice of the triplet loss function, the selection of samples to include in the training triplets and the batch size. To do that, we have adapted and retrained a network with omnidirectional images, which have been captured in an indoor environment with a catadioptric camera and have been converted into a panoramic format. The experiments conducted demonstrate that triplet networks improve substantially the performance in the visual localization task. However, the right choice of the studied factors is of great importance to fully exploit the potential of such architectures Fri, 11 Jul 2025 11:54:16 GMT https://hdl.handle.net/11000/36841 2025-07-11T11:54:16Z https://hdl.handle.net/11000/36840 Título : Evaluation of Open-Source OCR Libraries for Scene Text Recognition in the Presence of Fisheye Distortion Autor : Flores, María; Valiente, David; Alfaro, Marcos; Fabregat-Jaénn, Marc; Payá, Luis Resumen : Due to the rich and precise semantic information that text provides, scene text recognition is relevant in a wide range of vision-based applications. In recent years, the use of vision systems that combine a camera and a fisheye lens is common in a variety of applications. The addition of a fisheye lens has the great advantage of capturing a wider field of view, but this causes a great deal of distortion, making certain tasks challenging. In many applications, such as localization or mapping for a mobile robot, the algorithms work directly with fisheye images (i.e. distortion is not corrected). For this reason, the principal objective of this work is to study the effectiveness of some OCR (Optical Character Recognition) open-source libraries applied to images with fisheye distortion. Since no scene text dataset of this kind of image has been found, this work also generates a synthetic image dataset. A fisheye model which varies some parameters is applied to standard images of a benchmark scene text dataset to generate the proposed dataset. Fri, 11 Jul 2025 11:52:10 GMT https://hdl.handle.net/11000/36840 2025-07-11T11:52:10Z https://hdl.handle.net/11000/36839 Título : Augmented Feasibility Maps: A Simultaneous Approach to Redundancy Resolution and Path Planning Autor : Fabregat-Jaén, Marc; Peidró, Adrián; González-Amorós, Esther; Flores, María; Reinoso, Oscar Resumen : Redundant robotic manipulators are capable of performing complex tasks with an unprecedented level of dexterity and precision. However, their redundancy also introduces significant computational challenges, particularly in the realms of redundancy resolution and path planning. This paper introduces a novel approach to simultaneously address these challenges through the concept of Augmented Feasibility Maps, by integrating task coordinates as decision variables into the traditional feasibility maps. We validate the AFM concept by using Rapidly-Exploring Random Trees to explore the maps, demonstrating its efficacy in simulations of various dimensionalities. The method is capable of incorporating kinematic constraints, such as obstacle avoidance while adhering to joint limits. Fri, 11 Jul 2025 10:08:51 GMT https://hdl.handle.net/11000/36839 2025-07-11T10:08:51Z https://hdl.handle.net/11000/36837 Título : A Method for the Calibration of a LiDAR and Fisheye Camera System Autor : Martínez Ballester, Álvaro; Santo, Antonio; Ballesta, Monica; Gil, Arturo; Payá, Luis Resumen : LiDAR and camera systems are frequently used together to gain a more complete understanding of the environment in different fields, such as mobile robotics, autonomous driving, or intelligent surveillance. Accurately calibrating the extrinsic parameters is crucial for the accurate fusion of the data captured by both systems, which is equivalent to finding the transformation between the reference systems of both sensors. Traditional calibration methods for LiDAR and camera systems are developed for pinhole cameras and are not directly applicable to fisheye cameras. This work proposes a target-based calibration method for LiDAR and fisheye camera systems that avoids the need to transform images to a pinhole camera model, reducing the computation time. Instead, the method uses the spherical projection of the image, obtained with the intrinsic calibration parameters and the corresponding point cloud for LiDAR–fisheye calibration. Thus, unlike a pinhole-camerabased system, a wider field of view is provided, adding more information, which will lead to a better understanding of the environment itself, as well as enabling using fewer image sensors to cover a wider area. Fri, 11 Jul 2025 09:44:36 GMT https://hdl.handle.net/11000/36837 2025-07-11T09:44:36Z