Performance Analysis of the Multi-pass Transformation for Complex 3D-Stencils on GPUs

Artículo presentado al Congreso Español de Informática 2013Performance Analysis of the Multi-pass Transformation for Complex 3D-Stencils on GPU

Siting Multiple Observers for Maximum Coverage: An Accurate Approach

The selection of the minimal number of observers that ensures the maximum visual coverage over an area represented by a digital elevation model (DEM) have great interest in many elds, e.g., telecommunications, environment planning, among others. However, this problem is complex and intractable when the number of points of the DEM is relatively high. This complexity is due to three issues: 1) the di culty in determining the visibility of the terrain from one point, 2) the need to know the visibility at all points of the terrain and 3) the combinatorial complexity of the selection of observers. The recent progress in total-viewshed maps computation not only provides an e cient solu- tion to the rst two problems, but also opens other ways to new solutions that were unthinkable previously. This paper presents a new type of cartography, called the masked total viewshed map, and provides optimal solutions for both sequential and simultaneous observers location.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

16 Cards to Get Into Computer Organization

This paper presents a novel educative activity for teaching computer architecture fundamentals. This activity is actually a game that uses 16 cards and involves about twenty active participant students. Executing this activity in the fi rst class of the course allows the studentin only 45 minutes to acquire the fundamental concepts of computer organization. The results of the surveys that evaluate the proposed activity together with the grades obtained by the students at the end of course corroborate the importance of the proposed game in the assimilation of more complex concepts in computer architecture.Universidad de Granada: Departamento de Arquitectura y Tecnología de Computadores; Vicerrectorado para la Garantía de la Calidad

VPP: Visibility-Based Path Planning Heuristic for Monitoring Large Regions of Complex Terrain Using a UAV Onboard Camera

This work was partially supported by the Spanish Ministry of Science and Technology through the projects TIN2016-80920-R and PID2019-105396RB-I00, the Regional Government of Andalusia through the project A-TIC-458-UGR18 (DeepL-ISCO) within the Andalucia ERDF2014-20 Operational Programme, and the University of Malaga through the I Plan Propio de Investigacion.The use of unmanned aerial vehicles with multiple onboard sensors has grown significantly in tasks involving terrain coverage such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximizing the land covered from the flight path a challenging objective, especially when the area to bemonitored is irregular, large and includesmany blind spots. Accordingly, state-of-the-art total viewshed algorithms can be of great help to analyze large areas and find new paths providing maximum visibility. This article shows how the total viewshed computation is a valuable tool for generating paths that provide maximum visibility during a flight. We introduce a new heuristic called visibility-based path planning (VPP) that offers a different solution to the path planning problem. VPP identifies the hidden areas of the target territory to generate a path that provides the highest visual coverage. Simulation results show that VPP can cover up to 98.7% of theMontes deMalaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located within the province of Malaga (Spain) and chosen as regions of interest. In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.Spanish Government TIN2016-80920-R PID2019-105396RB-I00Regional Government of Andalusia within the Andalucia ERDF2014-20 Operational Programme A-TIC-458-UGR18University of Malaga through the I Plan Propio de Investigacio

Acelerando la comparación de huellas dactilares basadas en agrupaciones deformables de minucias

El reconocimiento de huellas dactilares es considerado como uno de los m´etodos de acreditaci´on biom´etrica m´as utilizado en la actualidad. La identificaci´on de una huella requiere realizar la comparaci´on de sus minucias con todas las minucias que conforman cada huella perteneciente a una base de datos. Los algoritmos de comparaci´on de huellas m´as avanzados son muy costosos desde el punto de vista computacional, e ineficientes cuando trabajan sobre bases de datos de grandes dimensiones. En este trabajo, se han incluido diversos m´etodos para acelerar el algoritmo DMC (el m´etodo de comparaci´on de huellas dactilares m´as preciso basado ´unicamente en minucias). En particular, se han reescrito en C++ las funciones del algoritmo con mayor carga computacional; se ha creado una librer´ıa est´atica en C++ donde se ejecuta el algoritmo de comparaci´on DMC modificado y que se conecta con el c´odigo original en C# utilizando para ello un proyecto de librer´ıa de clases de CLR. La soluci´on reimplementa funciones cr´ıticas tales como la cuenta del n´umero de bits con valor uno mediante la inclusi´on de una librer´ıa de PopCount en C++ y el uso del cuadrado de la distancia Eucl´ıdea para el c´alculo de la vecindad de las minucias. Los resultados experimentales muestran una reducci´on significativa del tiempo de ejecuci´on de las funciones optimizadas dentro del algoritmo DMC. Por ´ultimo, se presenta como trabajo futuro una nueva estrategia de procesamiento paralelo de los datos de las huellas, en la que se tiene en cuenta la jerarqu´ıa de memoria.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Where Have the Litigants Gone?

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups. For this reason, the classification of coral species has always required an aid from a domain expert. The objective of this paper is to develop an accurate classification model for coral texture images. Current datasets contain a large number of imbalanced classes, while the images are subject to inter-class variation. We have analyzed 1) several Convolutional Neural Network (CNN) architectures, 2) data augmentation techniques and 3) transfer learning. We have achieved the state-of-the art accuracies using different variations of ResNet on the two current coral texture datasets, EILAT and RSMAS.Comment: 22 pages, 10 figure

FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations

It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i.e., the network becomes highly biased to the data it has been trained on. This issue is often alleviated using transfer learning, regularization techniques and/or data augmentation. This work presents a new approach, independent but complementary to the previous mentioned techniques, for improving the generalization of DNNs on very small datasets in which the involved classes share many visual features. The proposed model, called FuCiTNet (Fusion Class inherent Transformations Network), inspired by GANs, creates as many generators as classes in the problem. Each generator, k, learns the transformations that bring the input image into the k-class domain. We introduce a classification loss in the generators to drive the leaning of specific k-class transformations. Our experiments demonstrate that the proposed transformations improve the generalization of the classification model in three diverse datasets.This work partially supported by the Spanish Ministry of Science and Technology under the project TIN2017-89517-P and the project TEC2016-75976-R, financed by the Spanish Ministerio de Economía, Industria y Competitividad and the European Regional Development Fund (ERDF). S. Tabik was supported by the Ramon y Cajal Programme (RYC-2015-18136). E.G was supported by the European Research Council (ERC Grant agreement 647038 [BIODESERT]), with additional support from Generalitat Valenciana (CIDEGENT/2018/041)

A snapshot of image pre-processing for convolutional neural networks: case study of MNIST

In the last five years, deep learning methods and particularly Convolutional Neural Networks (CNNs) have exhibited excellent accuracies in many pattern classification problems. Most of the state-of-the-art models apply data-augmentation techniques at the training stage. This paper provides a brief tutorial on data preprocessing and shows its benefits by using the competitive MNIST handwritten digits classification problem. We show and analyze the impact of different preprocessing techniques on the performance of three CNNs, LeNet, Network3 and DropConnect, together with their ensembles. The analyzed transformations are, centering, elastic deformation, translation, rotation and different combinations of them. Our analysis demonstrates that data-preprocessing techniques, such as the combination of elastic deformation and rotation, together with ensembles have a high potential to further improve the state-of-the-art accuracy in MNIST classification.Spanish Government TIN2014-524 57251-PAndalusian Research Plans P11-TIC-7765Spanish Government RYC-2015-1813