Abnormality detection using graph matching for multi-task dynamics of autonomous systems

Self-learning abilities in autonomous systems are essential to improve their situational awareness and detection of normal/abnormal situations. In this work, we propose a graph matching technique for activity detection in autonomous agents by using the Gromov-Wasserstein framework. A clustering approach is used to discretise continuous agents' states related to a specific task into a set of nodes with similar objectives. Additionally, a probabilistic transition matrix between nodes is used as edges weights to build a graph. In this paper, we extract an abnormal area based on a sub-graph that encodes the differences between coupled of activities. Such sub-graph is obtained by applying a threshold on the optimal transport matrix, which is obtained through the graph matching procedure. The obtained results are evaluated through experiments performed by a robot in a simulated environment and by a real autonomous vehicle moving within a University Campus

Jammer Detection in Vehicular V2X Networks

Vehicle-to-Everything (V2X) is an emergent technology for enhancing traffic efficiency, road safety and autonomous driving. Vehicles interconnected with their prevalent wireless environment are prone to various security threats that might affect traffic and life safety mmensely. Jamming attacks, a legacy and dated problem, still persists much to the havoc of V2X communications. The following paper proposes a framework for jammer detection adapted to V2X communications scenario. A Generalized Dynamic Bayesian network is used to learn the V2X signal environment in a statistical manner. Subsequently, a Modified Markov Jump Particle filter (M-MJPF) is used for signal predictions where the innovations in the observed signal versus the predicted signal enable our framework to detect the jammer. Simulation results highlight the efficacy and accuracy of our approach in V2X jammer detection

An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

In this paper, we propose to introduce an emergent Self-Awareness (SA) module at the physical layer (PHY) in Cognitive Unmanned Aerial Vehicle (UAV) Radios to improve PHY security, especially against jamming attacks. SA is based on learning a hierarchical representation of the radio environment by means of a proposed Hierarchical Dynamic Bayesian Network (HDBN). It is shown how the acquired knowledge from previous experiences facilitate the radio spectrum perception and allow the radio to detect abnormal behaviours caused by jamming attacks. Detecting abnormalities realize a fundamental step towards growing up incrementally the radio\u2019s long-term memory. Deviations from predictions estimated during abnormal situations are used to characterize jammers at multiple levels and discover their dynamic behavioural rules. Besides, a proactive consequence can be drawn after estimating the jammer\u2019s signal to act efficiently by mitigating its effects on the received stimuli. Simulation results show that the introduction of the novel SA functionalities with the proposed HDBN framework provides the high accuracy of characterizing, detecting and predicting the jammer\u2019s activities

Effects of ambient lighting displays on peripheral activity awareness

The emergence of ubiquitous sensing and the Internet of Things (IoT) have inspired the development of “smart” everyday objects, which offer tremendous opportunities for maintaining the quality of life in ambient assisted living (AAL) environments. Inspired by the future possibilities of connected everyday devices, we envision a peripheral activity-based awareness system that captures human activity information and renders this information to enhance context awareness and support social connectedness between the elderly and their caregivers. Leveraging ambient intelligence and IoT technologies, ambient displays can convey activity information in the periphery of our attention. In particular, light has been used as a means to display ambient information and there is scientific evidence that it can enhance well-being, interconnectedness, and improve productivity. In this paper, we undertake two studies, first through an exploratory study we investigate the features of light suitable for conveying subtle activity information within the periphery of the users' attention for promoting context awareness. Also, we examine the preferences, perceptions, and interpretations of ambient lighting configurations of prospective caregiver's for conveying the activity information of older adults. In a second study, we assess the implications of activity awareness through lighting on cognitive performance, moods, and social connectedness. The set-up and design decisions of the second study were partly informed by previous research and by the outcomes of the first experiment. Together, these studies provide additional design guidelines for representing activity information with ambient lighting and highlight potential benefits and usage possibilities for lighting displays within the AAL domain. Furthermore, the results indicate a significant effect of activity awareness on cognitive performance. However, there were no significant effects of activity awareness through lighting on moods and social connectedness. Finally, we discuss the design guidelines and implications of these findings for designing activity-based ambient lighting displays for AAL environments. <br/

A fast cardiac electromechanics model coupling the Eikonal and the nonlinear mechanics equations

We present a new model of human cardiac electromechanics for the left ventricle where electrophysiology is described by a Reaction-Eikonal model and which enables an off-line resolution of the reaction model, thus entailing a big saving of computational time. Subcellular dynamics is coupled with a model of tissue mechanics, which is in turn coupled with a Windkessel model for blood circulation. Our numerical results show that the proposed model is able to provide a physiological response to changes in certain variables (end-diastolic volume, total peripheral resistance, contractility). We also show that our model is able to reproduce with high accuracy and with a considerably lower computational time the results that we would obtain if the monodomain model should be used in place of the Eikonal model

Incremental learning of abnormalities in autonomous systems

In autonomous systems, self-awareness capabilities are useful to allow artificial agents to detect abnormal situations based on previous experiences. This paper presents a method that facilitates the incremental learning of new models by an agent. Available learned models can dynamically generate probabilistic predictions as well as evaluate their mismatch from current observations. Observed mismatches are grouped through an unsupervised learning strategy into different classes, each of them corresponding to a dynamic model in a given region of the state space. Such clusters define switching Dynamic Bayesian Networks (DBNs) employed for predicting future instances and detect anomalies. Inferences generated by several DBNs that use different sensorial data are compared quantitatively. For testing the proposed approach, it is considered the multi-sensorial data generated by a robot performing various tasks in a controlled environment and a real autonomous vehicle moving at a University Campus

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 (AIn), and a cognitive-UAV is employed as a case study. An Active Generalized Dynamic Bayesian Network (Active-GDBN) is proposed to represent the external environment that jointly encodes the physical signal dynamics and the dynamic interaction between UAV and jammer in the spectrum. We cast the action and planning as a Bayesian inference problem that can be solved by avoiding surprising states (minimizing abnormality) during online learning. Simulation results verify the effectiveness of the proposed AIn approach in minimizing abnormalities (maximizing rewards) and has a high convergence speed by comparing it with the conventional Frequency Hopping and Q-learnin