Virtual environment trajectory analysis:a basis for navigational assistance and scene adaptivity

This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context

Route Swarm: Wireless Network Optimization through Mobility

In this paper, we demonstrate a novel hybrid architecture for coordinating networked robots in sensing and information routing applications. The proposed INformation and Sensing driven PhysIcally REconfigurable robotic network (INSPIRE), consists of a Physical Control Plane (PCP) which commands agent position, and an Information Control Plane (ICP) which regulates information flow towards communication/sensing objectives. We describe an instantiation where a mobile robotic network is dynamically reconfigured to ensure high quality routes between static wireless nodes, which act as source/destination pairs for information flow. The ICP commands the robots towards evenly distributed inter-flow allocations, with intra-flow configurations that maximize route quality. The PCP then guides the robots via potential-based control to reconfigure according to ICP commands. This formulation, deemed Route Swarm, decouples information flow and physical control, generating a feedback between routing and sensing needs and robotic configuration. We demonstrate our propositions through simulation under a realistic wireless network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on Intelligent Robots and Systems (IROS) 201

A neuro-fuzzy monitoring system. Application to flexible production systems.

The multiple reconfiguration and the complexity of the modern production system lead to design intelligent monitoring aid systems. Accordingly, the use of neuro-fuzzy technics seems very promising. In this paper, we propose a new monitoring aid system composed by a dynamic neural network detection tool and a neuro-fuzzy diagnosis tool. Learning capabilities due to the neural structure permit us to update the monitoring aid system. The neuro-fuzzy network provides and abductive diagnosis. Moreover it takes into account the uncertainties on the maintenance knowledge by giving a fuzzy characterization of each cause. At the end, we illustrate the industrial usefulness of the proposed dynamic neuro-fuzzy monitoring system through a flexible production system monitoring application

Demand based State Aware Channel Reconfiguration Algorithm for Multi-Channel Multi-Radio Wireless Mesh Networks

Efficient utilization of Multi Channel - Multi Radio (MC-MR) Wireless Mesh Networks (WMNs) can be achieved only by intelligent Channel Assignment (CA) and Link Scheduling (LS). Due to the dynamic nature of traffic demand in WMNs, the CA has to be reconfigured whenever traffic demand changes, in order to achieve maximum throughput in the network. The reconfiguration of CA requires channel switching which leads to disruption of ongoing traffic in the network. The existing CA algorithms for MC-MR WMNs in the literature do not consider the channel reconfiguration overhead that occurs due to this channel switching. In this paper, we propose a novel reconfiguration framework that considers both network throughput and reconfiguration overhead to quantitatively evaluate a reconfiguration algorithm. Based on the reconfiguration framework, we propose an online heuristic algorithm for CA called Demand based State Aware channel Reconfiguration Algorithm (DeSARA) that finds the CA for the current traffic demand by considering the existing CA of the network to minimize the reconfiguration overhead. We show through simulations that DeSARA outperforms both static CA and fully dynamic CA in terms of total achieved throughput