Home | << 1 2 3 4 5 6 7 8 9 10 >> [11–16] |
Records | |||||
---|---|---|---|---|---|
Author | Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira | ||||
Title | Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives | Type | Journal Article | ||
Year | 2016 | Publication | Robotics and Autonomous Systems Journal | Abbreviated Journal | |
Volume | Vol. 83 | Issue | Pages | pp. 312-325 | |
Keywords | Incremental scene reconstructionPoint cloudsAutonomous vehiclesPolygonal primitives | ||||
Abstract | When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 49 | ||
Permanent link to this record | |||||
Author | G.A. Rubio; Wilton Agila | ||||
Title | Transients analysis in Proton Exchange Membrane Fuel Cells: A critical review | Type | Conference Article | ||
Year | 2019 | Publication | 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania | Abbreviated Journal | |
Volume | Issue | Pages | 249-252 | ||
Keywords | |||||
Abstract | When a proton exchange fuel cell operates it produces in addition to electrical energy, heat and water as sub products, which impact on the performance of the cell. This paper analyzes the issue of transients and proposes a model that describes the dynamic operation of the fuel cell. The model considers the transients produced by electrochemical reactions, by flow water and by heat transfer. Two-phase flow transients result in increased the parasitic power losses and thermal transients may result in flooding or dryout of the GDL and membrane, understanding transient behavior is critical for reliable and predictable performance from the cell. |
||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 111 | ||
Permanent link to this record | |||||
Author | Wilton Agila; Gomer Rubio; Francisco Vidal; B. Lima | ||||
Title | Real time Qualitative Model for estimate Water content in PEM Fuel Cell | Type | Conference Article | ||
Year | 2019 | Publication | 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania | Abbreviated Journal | |
Volume | Issue | Pages | 455-459 | ||
Keywords | |||||
Abstract | To maintain optimum performance of the electrical response of a fuel cell, a real time identification of the malfunction situations is required. Critical fuel cell states depend, among others, on the variable demand of electric load and are directly related to the membrane hydration level. The real time perception of relevant states in the PEM fuel cell states space, is still a challenge for the PEM fuel cell control systems. Current work presents the design and implementation of a methodology based upon fuzzy decision techniques that allows real time characterization of the dehydration and flooding states of a PEM fuel cell. Real time state estimation is accomplished through a perturbation-perception process on the PEM fuel cell and further on voltage oscillation analysis. The real time implementation of the perturbation-perception algorithm to detect PEM fuel cell critical states is a novelty and a step forwards the control of the PEM fuel cell to reach and maintain optimal performance. |
||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 109 | ||
Permanent link to this record | |||||
Author | Wilton Agila; Gomer Rubio; L. Miranda; L. Vázquez | ||||
Title | Qualitative Model of Control in the Pressure Stabilization of PEM Fuel Cell | Type | Conference Article | ||
Year | 2018 | Publication | 7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. | Abbreviated Journal | |
Volume | Issue | Pages | 1221-1226 | ||
Keywords | |||||
Abstract | This work describes an approximate reasoning technique to deal with the non-linearity that occurs in the stabilization of the pressure of anodic and cathodic gases of a proton exchange membrane fuel cell (PEM). The implementation of a supervisory element in the stabilization of the pressure of the PEM cell is described. The fuzzy supervisor is a reference control, it varies the value of the reference given to the classic low-level controller, Proportional – Integral – Derivative (PID), according to the speed of change of the measured pressure and the change in the error of the pressure. The objective of the fuzzy supervisor is to achieve a rapid response over time of the variable pressure, avoiding unwanted overruns with respect to the reference value. A comparative analysis is detailed with the classic PID control to evaluate the operation of the “fuzzy supervisor”, with different flow values and different sizes of active area of the PEM cell (electric power generated). |
||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 88 | ||
Permanent link to this record | |||||
Author | Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez | ||||
Title | SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. | Type | Journal Article | ||
Year | 2020 | Publication | Abbreviated Journal | In Sensors | |
Volume | Vol. 2020-August | Issue | 16 | Pages | pp. 1-23 |
Keywords | object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities | ||||
Abstract | This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images oers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the diculty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with dierent types of semantic segmentation networks and using the same evaluation metrics is also included. |
||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 14248220 | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 133 | ||
Permanent link to this record | |||||
Author | Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati; Ardakani Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi | ||||
Title | Thermal Image Super-Resolution Challenge – PBVS 2020 | Type | Conference Article | ||
Year | 2020 | Publication | The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) | Abbreviated Journal | |
Volume | 2020-June | Issue | 9151059 | Pages | 432-439 |
Keywords | |||||
Abstract | This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR) which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase. |
||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | English | Summary Language | Original Title | ||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 21607508 | ISBN | 978-172819360-1 | Medium | |
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 123 | ||
Permanent link to this record | |||||
Author | Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa | ||||
Title | Deep learning based camera pose estimation in multi-view environment. | Type | Conference Article | ||
Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
Volume | Issue | Pages | 224-228 | ||
Keywords | |||||
Abstract | This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 93 | ||
Permanent link to this record | |||||
Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
Title | Near InfraRed Imagery Colorization | Type | Conference Article | ||
Year | 2018 | Publication | 25 th IEEE International Conference on Image Processing, ICIP 2018 | Abbreviated Journal | |
Volume | Issue | Pages | 2237-2241 | ||
Keywords | |||||
Abstract | This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics.1 Index Terms—Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization. |
||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 81 | ||
Permanent link to this record | |||||
Author | Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Thermal Image Super-Resolution: a Novel Architecture and Dataset | Type | Conference Article | ||
Year | 2020 | Publication | The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 | Abbreviated Journal | |
Volume | 4 | Issue | Pages | 111-119 | |
Keywords | Thermal images, Far Infrared, Dataset, Super-Resolution. | ||||
Abstract | This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem. The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are available. |
||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-989758402-2 | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 121 | ||
Permanent link to this record | |||||
Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Cross-spectral image dehaze through a dense stacked conditional GAN based approach. | Type | Conference Article | ||
Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
Volume | Issue | Pages | 358-364 | ||
Keywords | |||||
Abstract | This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | gtsi @ user @ | Serial | 92 | ||
Permanent link to this record |