no code implementations • 6 Mar 2024 • Ali Krayani, Khalid Khan, Lucio Marcenaro, Mario Marchese, Carlo Regazzoni
This paper presents a novel self-supervised path-planning method for UAV-aided networks.
no code implementations • 6 Mar 2024 • Nobel J. William, Ali Krayani, Lucio Marcenaro, Carlo Regazzoni
The following paper proposes a novel Vehicle-to-Everything (V2X) network abnormality detection scheme based on Bayesian generative models for enhanced network self-awareness functionality at the Base station (BS).
no code implementations • 17 Oct 2023 • Felix Obite, Ali Krayani, Atm S. Alam, Lucio Marcenaro, Arumugam Nallanathan, Carlo Regazzoni
This paper investigates the design of joint subchannel and power allocation in an uplink UAV-based cognitive NOMA network.
no code implementations • 20 Sep 2023 • Felix Obite, Ali Krayani, Atm S. Alam, Lucio Marcenaro, Arumugam Nallanathan, Carlo Regazzoni
Given the surge in wireless data traffic driven by the emerging Internet of Things (IoT), unmanned aerial vehicles (UAVs), cognitive radio (CR), and non-orthogonal multiple access (NOMA) have been recognized as promising techniques to overcome massive connectivity issues.
no code implementations • 1 Feb 2023 • Ali Krayani, Gabriele Barabino, Lucio Marcenaro, Carlo Regazzoni
This paper proposes a method to jointly detect GPS spoofing and jamming attacks in a V2X network.
no code implementations • 10 Aug 2022 • Ali Krayani, Atm S. Alam, Lucio Marcenaro, Arumugam Nallanathan, Carlo Regazzoni
This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\textit{AIn}$), and a cognitive-UAV is employed as a case study.
no code implementations • 29 Oct 2020 • Divya Thekke Kanapram, Pablo Marin-Plaza, Lucio Marcenaro, David Martin, Arturo de la Escalera, Carlo Regazzoni
In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models.
no code implementations • 28 Oct 2020 • Divya Thekke Kanapram, Fabio Patrone, Pablo Marin-Plaza, Mario Marchese, Eliane L. Bodanese, Lucio Marcenaro, David Martín Gómez, Carlo Regazzoni
A growing neural gas (GNG) algorithm is used to learn the node variables and conditional probabilities linking nodes in the DBN models; a Markov jump particle filter (MJPF) is employed for state estimation and abnormality detection in each agent using learned DBNs as filter parameters.
no code implementations • 28 Oct 2020 • Divya Kanapram, Damian Campo, Mohamad Baydoun, Lucio Marcenaro, Eliane L. Bodanese, Carlo Regazzoni, Mario Marchese
The proposed method produces multiple inference models by considering several features of the observed data.
no code implementations • 28 Oct 2020 • Divya Kanapram, Pablo Marin-Plaza, Lucio Marcenaro, David Martin, Arturo de la Escalera, Carlo Regazzoni
The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness.
no code implementations • 2 Jun 2020 • Damian Campo, Giulia Slavic, Mohamad Baydoun, Lucio Marcenaro, Carlo Regazzoni
This paper proposes a method for performing continual learning of predictive models that facilitate the inference of future frames in video sequences.
no code implementations • 17 Mar 2020 • Giulia Slavic, Damian Campo, Mohamad Baydoun, Pablo Marin, David Martin, Lucio Marcenaro, Carlo Regazzoni
This paper proposes a method for detecting anomalies in video data.
no code implementations • 9 Sep 2019 • Damian Campo, Vahid Bastani, Lucio Marcenaro, Carlo Regazzoni
This work proposes a novel method for estimating the influence that unknown static objects might have over mobile agents.
no code implementations • 9 Sep 2019 • Damian Campo, Alejandro Betancourt, Lucio Marcenaro, Carlo Regazzoni
This paper presents a methodology that aims at the incremental representation of areas inside environments in terms of attractive forces.
no code implementations • ICLR 2018 • Juan Sebastian Olier, Emilia Barakova, Matthias Rauterberg, Carlo Regazzoni
In the first level, a model learns representations to generate observed data.
no code implementations • 31 Aug 2017 • Mahdyar Ravanbakhsh, Moin Nabi, Enver Sangineto, Lucio Marcenaro, Carlo Regazzoni, Nicu Sebe
In this paper we address the abnormality detection problem in crowded scenes.
Ranked #4 on Abnormal Event Detection In Video on UCSD Ped2
no code implementations • 21 Nov 2016 • Mahdyar Ravanbakhsh, Hossein Mousavi, Moin Nabi, Lucio Marcenaro, Carlo Regazzoni
We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space.
no code implementations • 29 Sep 2016 • Mahdyar Ravanbakhsh, Hossein Mousavi, Moin Nabi, Mohammad Rastegari, Carlo Regazzoni
To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN.
no code implementations • 30 Aug 2016 • Vahid Bastani, Lucio Marcenaro, Carlo Regazzoni
An incremental/online state dynamic learning method is proposed for identification of the nonlinear Gaussian state space models.
no code implementations • 21 Jul 2016 • Alejandro Betancourt, Pietro Morerio, Emilia Barakova, Lucio Marcenaro, Matthias Rauterberg, Carlo Regazzoni
Due to their favorable location, wearable cameras frequently capture the hands of the user, and may thus represent a promising user-machine interaction tool for different applications.
1 code implementation • 30 Mar 2016 • Alejandro Betancourt, Natalia Díaz-Rodríguez, Emilia Barakova, Lucio Marcenaro, Matthias Rauterberg, Carlo Regazzoni
Wearable cameras stand out as one of the most promising devices for the upcoming years, and as a consequence, the demand of computer algorithms to automatically understand the videos recorded with them is increasing quickly.