Christian Mata

Hospital Sant Joan de Déu Barcelona

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Elsa Pastor

Universitat Politècnica de Catalunya

Eulàlia Planas

Universitat Politècnica de Catalunya

Gilberto Ochoa‐Ruiz

Tecnológico de Monterrey

Miguel González-Mendoza

Tecnológico de Monterrey

Joan Martı́

University of Girona

Arnau Oliver

University of Girona

Mario M. Valero

San Jose State University

Alain Lalande

Université de Bourgogne

Joaquim Casal

Universitat Politècnica de Catalunya

Adriana Palacios

Universidad de las Américas Puebla

Cooperative Institutions

Universitat Politècnica de Catalunya

Tecnológico de Monterrey

University of Girona

Institut de Recerca Sant Joan de Déu

Universitat de Barcelona

Université de Bourgogne

Hospital Sant Joan de Déu Barcelona

Sant Joan de Déu Research Foundation

Rocky Mountain Research (United States)

Rocky Mountain Research Station

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Remote Sensing (2020)

Mario M. Valero Steven Verstockt Christian Mata Dan Jimenez Lloyd Queen

Aerial Thermal Infrared (TIR) imagery has demonstrated tremendous potential to monitor active forest fires and acquire detailed information about fire behavior. However, aerial video is usually unstable and requires inter-frame registration before further processing. Measurement of image misalignment is an essential operation for video stabilization. Misalignment can usually be estimated through image similarity, although image similarity metrics are also sensitive to other factors such as changes in the scene and lighting conditions. Therefore, this article presents a thorough analysis of image similarity measurement techniques useful for inter-frame registration in wildfire thermal video. Image similarity metrics most commonly and successfully employed in other fields were surveyed, adapted, benchmarked and compared. We investigated their response to different camera movement components as well as recording frequency and natural variations in fire, background and ambient conditions. The study was conducted in real video from six fire experimental scenarios, ranging from laboratory tests to large-scale controlled burns. Both Global and Local Sensitivity Analyses (GSA and LSA, respectively) were performed using state-of-the-art techniques. Based on the obtained results, two different similarity metrics are proposed to satisfy two different needs. A normalized version of Mutual Information is recommended as cost function during registration, whereas 2D correlation performed the best as quality control metric after registration. These results provide a sound basis for image alignment measurement and open the door to further developments in image registration, motion estimation and video stabilization for aerial monitoring of active wildland fires.

Similarity (geometry)

Image registration

10.3390/rs12030540

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Citations (6)

Infrared Imaging Software for Jet Fire Analysis

Chemical engineering transactions (2018)

Christian Mata Elsa Pastor Borja Rengel Mario M. Valero Eulàlia Planas

Jet fires, often originated by the ignition of hydrocarbon sonic jets, are characterized by a high momentum jet flame lifted from the outlet orifice. The high thermal flux released, especially if there is flames impingement on a given equipment, can lead to a domino effect. Therefore, predicting its size and shape is quite interesting in order to foresee the possibility of this effect on a given equipment.Several mathematical models have been proposed to predict jet fires main features, some of them based on experimental data. However, the analysis of jet flames can be sometimes difficult. In this communication, an in-house infrared imaging software for jet fire analysis is presented, which allows the calculation of different flame-geometry descriptors. A set of capabilities are included in a graphical user-friendly interface that can help users to easily obtain fire metrics. The tool also provides options to export the results for a post-processing analysis useful for risk prevention, such as preventive design and calculation, and emergency protocols design.

Domino Effect

10.3303/cet1867147

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Citations (4)

Performance analysis of a self‐protection system for vehicles in case of WUI fire entrapment

Fire and Materials (2020)

Elsa Pastor Alba Àgueda Juan Sebastià Christian Mata Mario M. Valero

Summary We present the experimental performance analysis of a fire protection fabric for cars designed to protect people's life in case of fire entrapment. Two experimental burns were executed to simulate heat and smoke exposure conditions in case of vehicle entrapment in a rural road. For the first experimental burn, a 2‐m high fuel bed of pine slash was arranged in a 13 m × 6 m area. Fire was ignited at one end of the fuel bed and spread wind driven (3 m/s midflame wind speed). 2.8 m away from the other fuel bed end, a car covered with the fabric was placed parallel to the fire. Data analysis provided values of fire behavior and flame characteristics, being typical of wildfires of moderate intensity (1800 kW/m). Maximum air temperatures inside the vehicle ranged around 41°C to 42.5°C, providing evidence of the fabric's good performance. To evaluate the degree of protection against smoke, air change rates were estimated with and without the fabric covering the car. Also, an experimental burn similar to the previous one was executed to monitor CO 2 and CO concentrations inside the car. Tenable conditions for these gases were maintained during the whole test according to reviewed exposure criteria.

Fire Dynamics Simulator

10.1002/fam.2836

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Semi-Automatic GUI Platform to Characterize Brain Development in Preterm Children Using Ultrasound Images

Journal of Imaging (2023)

David Rabanaque María Regalado Raúl Benítez Sonia Rabanaque Thaïs Agut

The third trimester of pregnancy is the most critical period for human brain development, during which significant changes occur in the morphology of the brain. The development of sulci and gyri allows for a considerable increase in the brain surface. In preterm newborns, these changes occur in an extrauterine environment that may cause a disruption of the normal brain maturation process. We hypothesize that a normalized atlas of brain maturation with cerebral ultrasound images from birth to term equivalent age will help clinicians assess these changes. This work proposes a semi-automatic Graphical User Interface (GUI) platform for segmenting the main cerebral sulci in the clinical setting from ultrasound images. This platform has been obtained from images of a cerebral ultrasound neonatal database images provided by two clinical researchers from the Hospital Sant Joan de Déu in Barcelona, Spain. The primary objective is to provide a user-friendly design platform for clinicians for running and visualizing an atlas of images validated by medical experts. This GUI offers different segmentation approaches and pre-processing tools and is user-friendly and designed for running, visualizing images, and segmenting the principal sulci. The presented results are discussed in detail in this paper, providing an exhaustive analysis of the proposed approach's effectiveness.

Graphical user interface

Brain Development

10.3390/jimaging9070145

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FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation

arXiv (Cornell University) (2023)

Pablo Cesar Quihui-Rubio Daniel Flores-Araiza Miguel González-Mendoza Christian Mata Gilberto Ochoa‐Ruiz

This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis. The proposed model is compared to seven different U-Net-based architectures. The automatic segmentation performance of each model of the central zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor were evaluated using Dice Score (DSC), and the Intersection over Union (IoU) metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of 76.9% in the test set, outperforming most of the studied models in this work except from R2U-Net and attention R2U-Net architectures.

Pyramid (geometry)

Feature (linguistics)

Net (polyhedron)

Dice

10.48550/arxiv.2309.01322

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Citations (0)

Lets Chat: Leveraging Chatbot Outreach for Improved Course Performance

AEA Randomized Controlled Trials (2024)

Katharine Meyer Lindsay C. Page Christian Mata

Chatbot

Course (navigation)

Outreach

10.1257/rct.14833-1.0

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Toward the New Generation of Surgical Meshes with 4D Response: Soft, Dynamic, and Adaptable

Advanced Functional Materials (2020)

Sonia Lanzalaco Pau Turón Christine Weis Christian Mata Eulàlia Planas

Abstract Herein, a facile approach toward transforming a 2D polypropylene flexible mesh material into a 4D dynamic system is presented. The versatile platform, composed by a substrate of knitted fibers of isotactic polypropylene (iPP) mesh and a coating of thermosensitive poly( N ‐isopropylacrylamide‐co‐ N,N’ ‐methylene bis(acrylamide) (PNIPAAm‐ co ‐MBA) hydrogel, covalently bonded to the mesh surface, after cold‐plasma surface treatment and radical polymerization, is intended to undergo variations in its geometry via its reversible folding/unfolding behavior. The study is the first to trace the 3D movement of a flat surgical mesh, intended to repair hernia defects, under temperature and humidity control. An infrared thermographic camera and an optical microscope are used to evaluate the macroscopic and microscopic structure stimulus response. The presence of the PP substrate and the distribution of the gel surrounding the PP threads, affect both the PNIPAAM gel expansion/contraction as well as the time of folding/unfolding response. Furthermore, PP‐ g ‐PNIPAAm meshes show an increase in the bursting strength of ≈16% with respect to the uncoated mesh, offering a strongest and adaptable system for its future implantation in human body. The findings reported offer unprecedented application possibilities in the biomedical field.

10.1002/adfm.202004145

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Citations (26)

Main specifications of CFD codes for WUIVIEW activities

Mario M. Valero Christian Mata Juan Antonio Navarro Giordano Emrys Scarponi Elsa Pastor

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Image Enhancement and Segmentation of Magnetic Resonance Cerebral Vessels Through Conventional and Deep Learning Techniques

Daniela Herrera Francisco Lopez-Tiro Josep Munuera Christian Mata Miguel González-Mendoza

10.1109/cbms61543.2024.00083

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Assessment of the predictive capabilities of different modelling tools to forecast fire effects in residential compartments

Borja Rengel Elsa Pastor Eulàlia Planas Albert Bartolomé Christian Mata

Residential occupancies are the most common type of buildings where compartment fires occur. In order to provide fire protection in these occupancies and thus to significantly reduce the number of future victims, the prediction of the fire effects during the first minutes becomes crucial. Particularly, forecasting fire dynamics (i.e. fire growth, smoke dispersion, etc.) may send early warnings to occupants and may provide important information for the fire service rescue team. Nevertheless, validation analysis should be previously performed to assess the predictive capabilities of different modelling tools when determining the harmful fire effects in residential compartments. The present paper describes three fire experiments undertaken in naturally-ventilated residential compartments. The aim of current work is to assess the fire effects prediction performance by means of different fire models compared to experimental data. The Q ? curves, which are derived from the temperatures measured inside the compartments, are employed as input data for the fire models used: an analytical model, a two-zone model (CFAST) and a field model (FDS). Recommendations on models choice are provided according to the results accuracy and the computational time required for the modelling tools employed. Preliminary outcomes reveal similar experimental fire behaviour in terms of temperatures, heat release rates and smoke layer heights among the three fire scenarios analysed. In addition, gas temperatures were correctly predicted with FDS; whereas the analytical model and CFAST were the most appropriate methods to forecast the smoke layer heights according to the experimental data collected.

Fire Dynamics Simulator

Predictive modelling

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