Browsing by Subject "Deep learning"
Now showing items 1-20 of 472
-
3D vehicle detection on an FPGA from LiDAR point clouds
(Association for Computing Machinery (ACM), 2019)
Conference report
Open AccessIn this paper is presented a deep neural network architecture designed to run on a field-programmable gate array (FPGA) for detection vehicle on LIDAR point clouds. This works present a network based on VoxelNet adapted ... -
A closer look at referring expressions for video object segmentation
(2023-01)
Article
Open AccessThe task of Language-guided Video Object Segmentation (LVOS) aims at generating binary masks for an object referred by a linguistic expression. When this expression unambiguously describes an object in the scene, it is ... -
A collaborative statistical actor-critic learning approach for 6G network slicing control
(Institute of Electrical and Electronics Engineers (IEEE), 2021)
Conference lecture
Open AccessArtificial intelligence (AI)-driven zero-touch massive network slicing is envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy would be extended to the final consumer in the form of advanced digital ... -
A comparison of deep learning methods for urban traffic forecasting using floating car data
(Elsevier, 2020)
Article
Open AccessCities today must address the challenge of sustainable mobility, and traffic state forecasting plays a key role in mitigating traffic congestion in urban areas. For example, predicting path travel time is a crucial issue ... -
A comprehensive review of the movement imaginary brain-computer interface methods: Challenges and future directions
(Academic Press, 2022)
Part of book or chapter of book
Restricted access - publisher's policyBrain-computer interface (BCI) aims to translate human intention into a control output signal. In motor-imaginary (MI) BCI, the imagination of movement modifies the cortex brain activity. Such activities are then used in ... -
A Convolutional Neural Network for the automatic diagnosis of collagen VI-related muscular dystrophies
(2019-12-01)
Article
Open AccessThe development of machine learning systems for the diagnosis of rare diseases is challenging, mainly due to the lack of data to study them. This paper surmounts this obstacle and presents the first Computer-Aided Diagnosis ... -
A cross-layer review of deep learning frameworks to ease their optimization and reuse
(Institute of Electrical and Electronics Engineers (IEEE), 2020)
Conference report
Open AccessMachine learning and especially Deep Learning (DL) approaches are at the heart of many domains, from computer vision and speech processing to predicting trajectories in autonomous driving and data science. Those approaches ... -
A dataset of microscopic peripheral blood cell images for development of automatic recognition systems
(Elsevier, 2020-06)
Article
Open AccessThis article makes available a dataset that was used for the development of an automatic recognition system of peripheral blood cell images using convolutional neural networks [1]. The dataset contains a total of 17,092 ... -
A deep analysis on age estimation
(2015-12-15)
Article
Open AccessThe automatic estimation of age from face images is increasingly gaining attention, as it facilitates applications including advanced video surveillance, demographic statistics collection, customer profiling, or search ... -
A deep learning approach for automatic recognition of abnormalities in the cytoplasm of neutrophils
(Elsevier, 2024-06)
Article
Open AccessBackground and objectives: This study aims to develop and evaluate NeuNN, a system based on convolutional neural networks (CNN) and generative adversarial networks (GAN) for the automatic identification of normal neutrophils ... -
A deep learning approach for segmentation of red blood cell images and malaria detection
(2020-06-13)
Article
Open AccessMalaria is an endemic life-threating disease caused by the unicellular protozoan parasites of the genus Plasmodium. Confirming the presence of parasites early in all malaria cases ensures species-specific antimalarial ... -
A Deep Learning Based Approach to Automated App Testing
(Universitat Politècnica de Catalunya, 2020-09-09)
Master thesis
Open AccessMobile applications are worldwide extended. We use them for everything, from texting friends to managing our money. This boom has led to the emergence of companies dedicated exclusively to the development of mobile ... -
A deep learning pproach for the morphological recognition of reactive lymphocytes in patients with COVID-19 infection
(2022-05-23)
Article
Open AccessLaboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive ... -
A deep learning-based method for uncovering GPCR ligand-induced conformational states using interpretability techniques
(Springer, 2022)
Conference report
Open AccessThere is increasing interest in the development of tools for investigating the protein ligand space. Understanding the underlying mechanisms of G protein-coupled receptors (GPCR) in the ligand-binding process is of particular ... -
A deep learning-based modeling of a 270 V -to- 28 V DC-DC converter used in more electric aircrafts
(Institute of Electrical and Electronics Engineers (IEEE), 2021-07-21)
Article
Open AccessThis paper presents a novel approach for black-box modelling of 270 V -to- 28 V DC-DC step-down converters used in more electric aircrafts (MEA). These converters normally feed constant power loads (CPL). The proposed deep ... -
A deep q network-based multi-connectivity algorithm for heterogeneous 4G/5G cellular systems
(Springer, 2022)
Conference report
Restricted access - publisher's policyMulti-connectivity, which allows a user equipment to be simultaneously connected to multiple cells from different radio access network nodes that can be from a single or multiple radio access technologies, has emerged as ... -
A deep Q-network-based algorithm for multi-connectivity optimization in heterogeneous cellular-networks †
(2022-08-01)
Article
Open AccessThe use of multi-connectivity has become a useful tool to manage the traffic in heterogeneous cellular network deployments, since it allows a device to be simultaneously connected to multiple cells. The proper exploitation ... -
A differential privacy protection-based federated deep learning framework to fog-embedded architectures
(Elsevier, 2024-04)
Article
Open AccessNowadays, companies collect massive quantities of data to enhance their operations, often at the expense of sharing user sensible information. This data is widely used to train Deep Learning (DL) neural networks to model, ... -
A dual network for super-resolution and semantic segmentation of sentinel-2 imagery
(Multidisciplinary Digital Publishing Institute (MDPI), 2021-11-12)
Article
Open AccessThere is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. ... -
A multimodal approach using fundus images and text meta-data in a machine learning classifier with embeddings to predict years with self-reported diabetes: An exploratory analysis
(2024-06)
Article
Restricted access - publisher's policyAims: Machine learning models can use image and text data to predict the number of years since diabetes diagnosis; such model can be applied to new patients to predict, approximately, how long the new patient may have lived ...
