44 citations · 226 across the 30 of their papers we have counts for
31 papers
LiDAR-based Dynamic Blockage Prediction: A Data-driven Approach for Learning Interactive Bayesian Models
Saleemullah Memon, Ali Krayani, Pamela Zontone +3
Vehicular sensing-based intelligence has made substantial progress in transportation systems, leading to higher levels of safety and sustainability for smart cities and autonomous…
Flying by Inference: Active Inference World Models for Adaptive UAV Swarms
Kaleem Arshid, Ali Krayani, Lucio Marcenaro +2
This paper presents an expert-guided active-inference-inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi-UAV trajectory design from a…
Active Inference-Driven World Modeling for Adaptive UAV Swarm Trajectory Design
Kaleem Arshid, Ali Krayani, Lucio Marcenaro +2
This paper proposes an Active Inference-based framework for autonomous trajectory design in UAV swarms. The method integrates probabilistic reasoning and self-learning to enable di…
Bayesian Active Inference for Intelligent UAV Anti-Jamming and Adaptive Trajectory Planning
Ali Krayani, Seyedeh Fatemeh Sadati, Lucio Marcenaro +1
This paper proposes a hierarchical trajectory planning framework for UAVs operating under adversarial jamming conditions. Leveraging Bayesian Active Inference, the approach combine…
Self-Supervised Path Planning in UAV-aided Wireless Networks based on Active Inference
Ali Krayani, Khalid Khan, Lucio Marcenaro +2
This paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the result…
Interactive Bayesian Generative Models for Abnormality Detection in Vehicular Networks
Nobel J. William, Ali Krayani, Lucio Marcenaro +1
The following paper proposes a novel Vehicle-to-Everything (V2X) network abnormality detection scheme based on Bayesian generative models for enhanced network self-awareness functi…
Intelligent Resource Allocation for UAV-Based Cognitive NOMA Networks: An Active Inference Approach
Felix Obite, Ali Krayani, Atm S. Alam +3
Future wireless networks will need to improve adaptive resource allocation and decision-making to handle the increasing number of intelligent devices. Unmanned aerial vehicles (UAV…
Active Inference for Sum Rate Maximization in UAV-Assisted Cognitive NOMA Networks
Felix Obite, Ali Krayani, Atm S. Alam +3
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…
Integrated Sensing and Communication for Joint GPS Spoofing and Jamming Detection in Vehicular V2X Networks
Ali Krayani, Gabriele Barabino, Lucio Marcenaro +1
Vehicle-to-everything (V2X) communication is expected to be a prominent component of the sixth generation (6G) to accomplish intelligent transportation systems (ITS). Autonomous ve…
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach
Ali Krayani, Atm S. Alam, Lucio Marcenaro +2
This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference (), and a cognitive-UAV is employed as a case study…
Container Localisation and Mass Estimation with an RGB-D Camera
Tommaso Apicella, Giulia Slavic, Edoardo Ragusa +2
In the research area of human-robot interactions, the automatic estimation of the mass of a container manipulated by a person leveraging only visual information is a challenging ta…
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection
Divya Thekke Kanapram, Pablo Marin-Plaza, Lucio Marcenaro +3
The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the dete…
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection
Divya Kanapram, Pablo Marin-Plaza, Lucio Marcenaro +3
The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness. This paper aims to introdu…
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles
Divya Thekke Kanapram, Fabio Patrone, Pablo Marin-Plaza +5
The advancements in connected and autonomous vehicles in these times demand the availability of tools providing the agents with the capability to be aware and predict their own sta…
Dynamic Bayesian Approach for decision-making in Ego-Things
Divya Kanapram, Damian Campo, Mohamad Baydoun +4
This paper presents a novel approach to detect abnormalities in dynamic systems based on multisensory data and feature selection. The proposed method produces multiple inference mo…
Continual Learning of Predictive Models in Video Sequences via Variational Autoencoders
Damian Campo, Giulia Slavic, Mohamad Baydoun +2
This paper proposes a method for performing continual learning of predictive models that facilitate the inference of future frames in video sequences. For a first given experience,…
Learning Self-Awareness for Autonomous Vehicles: Exploring Multisensory Incremental Models
Mahdyar Ravanbakhsh, Mohamad Baydoun, Damian Campo +4
The technology for autonomous vehicles is close to replacing human drivers by artificial systems endowed with high-level decision-making capabilities. In this regard, systems must…
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models
Giulia Slavic, Damian Campo, Mohamad Baydoun +4
This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space…
Static force field representation of environments based on agents nonlinear motions
Damian Campo, Alejandro Betancourt, Lucio Marcenaro +1
This paper presents a methodology that aims at the incremental representation of areas inside environments in terms of attractive forces. It is proposed a parametric representation…
Incremental learning of environment interactive structures from trajectories of individuals
Damian Campo, Vahid Bastani, Lucio Marcenaro +1
This work proposes a novel method for estimating the influence that unknown static objects might have over mobile agents. Since the motion of agents can be affected by the presence…
Online Pedestrian Group Walking Event Detection Using Spectral Analysis of Motion Similarity Graph
Vahid Bastani, Damian Campo, Lucio Marcenaro +1
A method for online identification of group of moving objects in the video is proposed in this paper. This method at each frame identifies group of tracked objects with similar loc…
Audio-Based Search and Rescue with a Drone: Highlights from the IEEE Signal Processing Cup 2019 Student Competition
Antoine Deleforge, Diego Di Carlo, Martin Strauss +2
Unmanned aerial vehicles (UAV), commonly referred to as drones, have raised increasing interest in recent years. Search and rescue scenarios where humans in emergency situations ne…
Hierarchy of GANs for learning embodied self-awareness model
Mahdyar Ravanbakhsh, Mohamad Baydoun, Damian Campo +4
In recent years several architectures have been proposed to learn embodied agents complex self-awareness models. In this paper, dynamic incremental self-awareness (SA) models are p…
Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving
Mahdyar Ravanbakhsh, Mohamad Baydoun, Damian Campo +4
This paper presents a novel approach for learning self-awareness models for autonomous vehicles. The proposed technique is based on the availability of synchronized multi-sensor dy…