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This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations. Both observation types are used to characterize agents motion in a given environment. The proposed method generates locally uniform motion models by dividing a Gaussian process that approximates agents displacements on the scene and provides a Shared Level (SL) self-awareness based on Environment Centered (EC) models. Such models are then used to train in a semi-unsupervised way a set of Generative Adversarial Networks (GANs) that produce an estimation of external and internal parameters of moving agents. Obtained results exemplify the feasibility of using multi-perspective data for predicting and analyzing trajectory information.

This paper presents a novel approach for learning self-awareness models for autonomous vehicles. Proposed technique is based on the availability of synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a human operator. It is shown that different machine learning approaches can be used to first learn single modality models using coupled Dynamic Bayesian Networks; such models are then correlated at event level to discover contextual multimodal concepts. In the presented case, visual perception and localization are used as modalities. Cross-correlations among modalities in time is discovered from data and are described as probabilistic links connecting shared and private multi-modal DBNs at the event (discrete) level. Results are presented on experiments performed on an autonomous vehicle, highlighting potentiality of the proposed approach to allow anomaly detection and autonomous decision making based on learned self-awareness models.

Cognitive radio (CR) is a promising technology for future wireless spectrum allocation to improve the use of licensed bands. However, security challenges faced by cognitive radio technology are still a hot research topic. One of prevailing challenges is the radio frequency jamming attack, where adversaries are able to exploit on-the-fly reconfigurability potentials and learning mechanism of cognitive radios in order to devise and deploy advanced jamming tactics. Jamming attacks can significantly impact the performance of wireless communication systems and lead to significant overheads in terms of retransmission and increment of power consumption. In this context, a novel jammer detection algorithm is proposed using cyclic spectral analysis and artificial neural networks (ANN) for wide-band (WB) cognitive radios. The proposed approach assumes a WB spectrum occupied by various narrow-band (NB) signals, which can be either legitimate or jamming signals. The second order statistics, namely, the spectral correlation function (SCF) and ANN are used to classify each NB signal as a legitimate or jamming signal. The algorithm performance is shown with the help of simulations

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 of velocity fields ruling the dynamics of moving agents. It is assumed that attractive spots in the environment are responsible for modifying the motion of agents. A switching model is used to describe near and far velocity fields, which in turn are used to learn attractive characteristics of environments. The effect of such areas is considered radial over all the scene. Based on the estimation of attractive areas, a map that describes their effects in terms of their localizations, ranges of action, and intensities is derived in an online way. Information of static attractive areas is added dynamically into a set of filters that describes possible interactions between moving agents and an environment. The proposed approach is first evaluated on synthetic data; posteriorly, the method is applied on real trajectories coming from moving pedestrians in an indoor environment.