Articles de revista
Recent Submissions
-
Transfer-learning-based intrusion detection framework in IoT networks
(Multidisciplinary Digital Publishing Institute (MDPI), 2022-07-27)
Article
Open AccessCyberattacks in the Internet of Things (IoT) are growing exponentially, especially zero-day attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion detection systems (IDSs) adopted machine ... -
Small-layered feed-forward and convolutional neural networks for efficient P wave earthquake detection
(2022-11-15)
Article
Restricted access - publisher's policyThe number and efficiency of seismic networks have steadily increase over time delivering large datasets to be analyzed for earthquake occurrence. Automatic tools for accurate earthquake detection are under emerging and ... -
Fast and accurate SER estimation for large combinational blocks in early stages of the design
(Institute of Electrical and Electronics Engineers (IEEE), 2021-07-01)
Article
Open AccessSoft Error Rate (SER) estimation is an important challenge for integrated circuits because of the increased vulnerability brought by technology scaling. This paper presents a methodology to estimate in early stages of the ... -
A survey of deep learning techniques for cybersecurity in mobile networks
(2021-06-07)
Article
Open AccessThe widespread use of mobile devices, as well as the increasing popularity of mobile services has raised serious cybersecurity challenges. In the last years, the number of cyberattacks has grown dramatically, as well as ... -
Deep neural networks for earthquake detection and source region estimation in north-central Venezuela
(2020-10-01)
Article
Open AccessReliable earthquake detection algorithms are necessary to properly analyze and catalog the continuously growing seismic records. We report the results of applying a deep convolutional neural network, called UPC‐UCV ... -
Securing RSA hardware accelerators through residue checking
(2021-01)
Article
Open AccessCircuits for the hardware acceleration of cryptographic algorithms are ubiquitously deployed in consumer and industrial products. Although being secure from a mathematical point of view, such accelerators may expose several ... -
The RECIPE approach to challenges in deeply heterogeneous high performance systems
(2020-09)
Article
Open AccessRECIPE (REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems) is a recently started project funded within the H2020 FETHPC programme, which is expressly targeted at exploring ... -
Predictive reliability and fault management in exascale systems: State of the art and perspectives
(2020-09)
Article
Open AccessPerformance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due ... -
A cost-efficient QoS-aware analytical model of future software content delivery networks
(2021-07)
Article
Open AccessFreelance, part-time, work-at-home, and other flexible jobs are changing the concept of workplace, and bringing information and content exchange problems to companies. Geographically spread corporations may use remote ... -
Alternating direction implicit time integrations for finite difference acoustic wave propagation: parallelization and convergence
(Elsevier, 2020-06-15)
Article
Open AccessThis work studies the parallelization and empirical convergence of two finite difference acoustic wave propagation methods on 2-D rectangular grids, that use the same alternating direction implicit (ADI) time integration. ... -
On the use of probabilistic worst-case execution time estimation for parallel applications in high performance systems
(Multidisciplinary Digital Publishing Institute (MDPI), 2020-03-01)
Article
Open AccessSome high performance computing (HPC) applications exhibit increasing real-time requirements, which call for effective means to predict their high execution times distribution. This is a new challenge for HPC applications ... -
Distributed training of deep neural networks with spark: The MareNostrum experience
(Elsevier, 2019-07-01)
Article
Open AccessDeployment of a distributed deep learning technology stack on a large parallel system is a very complex process, involving the integration and configuration of several layers of both, general-purpose and custom software. ...
RSS 1.0