Search Results for author:
Found 21 papers, 1 papers with code
Self-Supervised Path Planning in UAV-aided Wireless Networks based on Active Inference
This paper presents a novel self-supervised path-planning method for UAV-aided networks.
Interactive Bayesian Generative Models for Abnormality Detection in Vehicular Networks
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).
Intelligent Resource Allocation for UAV-Based Cognitive NOMA Networks: An Active Inference Approach
This paper investigates the design of joint subchannel and power allocation in an uplink UAV-based cognitive NOMA network.
Active Inference for Sum Rate Maximization in UAV-Assisted Cognitive NOMA Networks
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.
Integrated Sensing and Communication for Joint GPS Spoofing and Jamming Detection in Vehicular V2X Networks
This paper proposes a method to jointly detect GPS spoofing and jamming attacks in a V2X network.
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach
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.
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection
In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models.
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles
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.
Dynamic Bayesian Approach for decision-making in Ego-Things
The proposed method produces multiple inference models by considering several features of the observed data.
Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection
The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness.
Continual Learning of Predictive Models in Video Sequences via Variational Autoencoders
This paper proposes a method for performing continual learning of predictive models that facilitate the inference of future frames in video sequences.
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models
This paper proposes a method for detecting anomalies in video data.
Incremental learning of environment interactive structures from trajectories of individuals
This work proposes a novel method for estimating the influence that unknown static objects might have over mobile agents.
Static force field representation of environments based on agents nonlinear motions
This paper presents a methodology that aims at the incremental representation of areas inside environments in terms of attractive forces.
Representing dynamically: An active process for describing sequential data
In the first level, a model learns representations to generate observed data.
Abnormal Event Detection in Videos using Generative Adversarial Nets
In this paper we address the abnormality detection problem in crowded scenes.
Ranked #4 on
Abnormal Event Detection In Video
on UCSD Ped2
Efficient Convolutional Neural Network with Binary Quantization Layer
We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space.
CNN-aware Binary Map for General Semantic Segmentation
To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN.
Incremental Nonlinear System Identification and Adaptive Particle Filtering Using Gaussian Process
An incremental/online state dynamic learning method is proposed for identification of the nonlinear Gaussian state space models.
Left/Right Hand Segmentation in Egocentric Videos
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.
Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos
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.
