RDF description Adrian Núñez-Marcos

PhD student


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Sep. 2016  -  Present
adrian.nunez [at] deusto.es

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[u' @inproceedings{nunez-marcos_using_2020, address = {P\xf3voa de Varzim, Portugal}, title = {Using {External} {Knowledge} to {Improve} {Zero}-{Shot} {Action} {Recognition} in {Egocentric} {Videos}}, doi = {10.1007/978-3-030-50347-5_16}, booktitle = {Proceedings of the 17th {International} {Conference} on {Image} {Analysis} and {Recognition}}, author = {N\xfa\xf1ez-Marcos, Adri\xe1n and Azkune, Gorka and Agirre, Eneko and L\xf3pez-de-Ipi\xf1a, Diego and Arganda-Carreras, Ignacio}, month = jun, year = {2020} }']

[u' @article{sanchez-corcuera_smart_2019, title = {Smart cities survey: {Technologies}, application domains and challenges for the cities of the future}, volume = {15}, issn = {1550-1477}, shorttitle = {Smart cities survey}, url = {https://doi.org/10.1177/1550147719853984}, doi = {10.1177/1550147719853984}, abstract = {The introduction of the Information and Communication Technologies throughout the last decades has created a trend of providing daily objects with smartness, aiming to make human life more comfortable. The paradigm of Smart Cities arises as a response to the goal of creating the city of the future, where (1) the well-being and rights of their citizens are guaranteed, (2) industry and (3) urban planning is assessed from an environmental and sustainable viewpoint. Smart Cities still face some challenges in their implementation, but gradually more research projects of Smart Cities are funded and executed. Moreover, cities from all around the globe are implementing Smart City features to improve services or the quality of life of their citizens. Through this article, (1) we go through various definitions of Smart Cities in the literature, (2) we review the technologies and methodologies used nowadays, (3) we summarise the different domains of applications where these technologies and methodologies are applied (e.g. health and education), (4) we show the cities that have integrated the Smart City paradigm in their daily functioning and (5) we provide a review of the open research challenges. Finally, we discuss about the future opportunities for Smart Cities and the issues that must be tackled in order to move towards the cities of the future.}, language = {en}, number = {6}, urldate = {2019-06-10}, journal = {International Journal of Distributed Sensor Networks}, author = {S\xe1nchez-Corcuera, Ruben and Nu\xf1ez-Marcos, Adri\xe1n and Sesma-Solance, Jesus and Bilbao-Jayo, Aritz and Mulero, Rub\xe9n and Zulaika, Unai and Azkune, Gorka and Almeida, Aitor}, month = jun, year = {2019}, keywords = {IF1.151, IoT, Q4, Survey, architecture, co-creation, e-government, futuraal, smart cities}, pages = {1550147719853984} }']

[u' @article{nunez-marcos_vision-based_2017, title = {Vision-{Based} {Fall} {Detection} with {Convolutional} {Neural} {Networks}}, issn = {1530-8669}, abstract = {One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has at- tracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have pro- vided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe the irruption of the Smart Environments and the Internet of Things paradigms, to- gether with the increasing number of cameras in our daily environment, conform an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a se- quence of frames contains a person falling. To model the video motion and make the system scenario-independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving state-of-the-art results in all three of them.}, journal = {Wireless Communications \\& Mobile Computing}, author = {N\xfa\xf1ez-Marcos, Adri\xe1n and Azkune, Gorka and Arganda-Carreras, Ignacio}, month = nov, year = {2017}, keywords = {Activity Recognition, Computer Vision, Deep Learning, Smart Environments, jcr0.869, q4}, pages = {25} }']