Visualitzant ICC_Articles per títol
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Parallelizing dense and banded linear algebra libraries using SMPSs
John Wiley (2009-12-25)The promise of future many-core processors, with hundreds of threads running concurrently, has led the developers of linear algebra libraries to rethink their design in order to extract more parallelism, further exploit ... -
Particle-filter-based Pose Estimation from Controlled Motion with Application to Visual Servoing
InTech (2014)In this paper, we present a Bayesian algorithm based on particle filters to estimate the camera pose for vision-based control. The state model is represented as a relative camera pose between the current and initial ... -
Paving the way towards high-level parallel pattern interfaces for data stream processing
Elsevier (2018-10)The emergence of the Internet of Things (IoT) data stream applications has posed a number of new challenges to existing infrastructures, processing engines, and programming models. In this sense, high-level interfaces, ... -
Performance Model of MapReduce Iterative Applications for Hybrid Cloud Bursting
IEEE (2018-02)Hybrid cloud bursting (i.e., leasing temporary off-premise cloud resources to boost the overall capacity during peak utilization) can be a cost-effective way to deal with the increasing complexity of big data analytics, ... -
Performance modeling of the sparse matrix–vector product via convolutional neural networks
Springer (2020-02-04)Modeling the execution time of the sparse matrix–vector multiplication (SpMV) on a current CPU architecture is especially complex due to (i) irregular memory accesses; (ii) indirect memory referencing; and (iii) low ... -
Performance versus energy consumption of hyperspectral unmixing algorithms on multi-core platforms
SpringerOpen (2013)Hyperspectral imaging is a growing area in remote sensing in which an imaging spectrometer collects hundreds of images (at different wavelength channels) for the same area on the surface of the Earth. Hyperspectral images ... -
Performance–energy trade‑ofs of deep learning convolution algorithms on ARM processors
Springer (2023)In this work, we assess the performance and energy efciency of high-performance codes for the convolution operator, based on the direct, explicit/implicit lowering and Winograd algorithms used for deep learning (DL) ... -
Pose Estimation Through Cue Integration: A Neuroscience-Inspired Approach
IEEE (2012)Primates possess a superior ability in dealing with objects in their environment. One of the keys for achieving such ability is the continuous concurrent use of multiple cues, especially of visual nature. This work is aimed ... -
Practical considerations for acoustic source localization in the IoT era: Platforms, energy efficiency, and performance
IEEE (2019-06)The rapid development of the Internet of Things (IoT) has posed important changes in the way emerging acoustic signal processing applications are conceived. While traditional acoustic processing applications have been ... -
Predicting grasp success in the real world - A study of quality metrics and human assessment
Elsevier (2019)Grasp quality metrics aim at quantifying different aspects of a grasp configuration between a specific robot hand and object. They produce a numerical value that allows to rank grasp configurations and optimize based on ... -
Predicting the internal model of a robotic system from its morphology
Elsevier (2018)The estimation of the internal model of a robotic system results from the interaction of its morphology, sensors and actuators, with a particular environment. Model learning techniques, based on supervised machine learning, ... -
Preface Special issue CogKnow
Elsevier (2015) -
Preliminary Work on a Virtual Reality Interface for the Guidance of Underwater Robots
MDPI (2020)The need for intervention in underwater environments has increased in recent years but there is still a long way to go before AUVs (Autonomous Underwater Vehicleswill be able to cope with really challenging missions. ... -
Process mining for healthcare: Characteristics and challenges
Elsevier (2022-02-09)Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on ... -
Process Model Metrics for Quality Assessment of Computer-Interpretable Guidelines in PROform
MDPI (2021-03-25)Background: Clinical Practice Guidelines (CPGs) include recommendations to optimize patient care and thus have the potential to improve the quality and outcomes of healthcare. To achieve this, CPG recommendations are usually ... -
Programming matrix algorithms-by-blocks for thread-level parallelism
Association for Computing Machinery (2009-07)With the emergence of thread-level parallelism as the primary means for continued improvement of performance, the programmability issue has reemerged as an obstacle to the use of architectural advances. We argue that ... -
Programming parallel dense matrix factorizations with look-ahead and OpenMP
Springer (2019)We investigate a parallelization strategy for dense matrix factorization (DMF) algorithms, using OpenMP, that departs from the legacy (or conventional) solution, which simply extracts concurrency from a multi-threaded ... -
Proteo: a framework for the generation and evaluation of malleable MPI applications
Springer (2024-07-02)Applying malleability to HPC systems can increase their productivity without degrading or even improving the performance of running applications. This paper presents Proteo, a confgurable framework that allows to design ... -
Prototipo físico-manipulativo: Material Didáctico de Apoyo a la Enseñanza y el Aprendizaje de la Geometría Analítica en el Espacio
Sociedad Canaria de Profesorado de Matemáticas Luis Balbuena Castellano (2023)El presente trabajo se centra en la creación de material manipulativo de apoyo a la enseñanza y el aprendizaje de la geometría analítica en el espacio como respuesta a una problemática concreta que es una constante en ... -
PyDTNN: A user-friendly and extensible framework for distributed deep learning
Springer (2021-02-22)We introduce a framework for training deep neural networks on clusters of computers with the following appealing properties: (1) It is developed in Python, exposing an amiable interface that provides an accessible entry ...