External circuit system effects on chlorine plasma instabilities

The effects of the external circuit on plasma instabilities in all inductive plasma source are investigated. The instabilities are found to be asymmetric with respect to the circuit input impedance. A simplified model of the antenna-plasnia coupling provides an explanation of the asymetry.

Probing warm dense lithium by inelastic X-ray scattering

One of the grand challenges of contemporary physics is understanding strongly interacting quantum systems comprising such diverse examples as ultracold atoms in traps, electrons in high-temperature superconductors and nuclear matter. Warm dense matter, defined by temperatures of a few electron volts and densities comparable with solids, is a complex state of such interacting matter. Moreover, the study of warm dense matter states has practical applications for controlled thermonuclear fusion, where it is encountered during the implosion phase, and it also represents laboratory analogues of astrophysical environments found in the core of planets and the crusts of old stars, Here we demonstrate how warm dense matter states can be diagnosed and structural properties can be obtained by inelastic X-ray scattering measurements on a compressed lithium sample. Combining experiments and ab initio simulations enables us to determine its microscopic state and to evaluate more approximate theoretical models for the ionic structure.

Evidence of Short-Range Screening in Shock-Compressed Aluminum Plasma

We have investigated the angular variation in elastic x-ray scattering from a dense, laser-shock-compressed aluminum foil. A comparison of the experiment with simulations using an embedded atom potential in a molecular dynamics simulation shows a significantly better agreement than simulations based on an unscreened one-component plasma model. These data illustrate, experimentally, the importance of screening for the dense plasma static structure factor.

Accurate available bandwidth estimation in IEEE 802.11-based ad hoc networks

Quality of Service (QoS) support in IEEE 802.11-based ad hoc networks relies on the networks’ ability to estimate the available bandwidth on a given link. However, no mechanism has been standardized to accurately evaluate this resource. This remains one of the main issues open to research in this field. This paper proposes an available bandwidth estimation approach which achieves more accurate estimation when compared to existing research. The proposed approach differentiates the channel busy caused by transmitting or receiving from that caused by carrier sensing, and thus improves the accuracy of estimating the overlap probability of two adjacent nodes’ idle time. Simulation results testify the improvement of this approach when compared with well known bandwidth estimation methods in the literature.

Rethinking available bandwidth estimation in IEEE 802.11-based ad hoc networks

This Letter rethinks the problems of available bandwidth estimation in IEEE 802.11-based ad hoc networks. The estimation accuracy is increased by improving the calculation accuracy that the probability for two adjacent nodes' idle periods toverlap (a key issue when estimating the available bandwidth in 802.11 networks)

Host-based P2P flow identification and use in real-time

Data identification is a key task for any Internet Service Provider (ISP) or network administrator. As port fluctuation and encryption become more common in P2P traffic wishing to avoid identification, new strategies must be developed to detect and classify such flows. This paper introduces a new method of separating P2P and standard web traffic that can be applied as part of a data mining process, based on the activity of the hosts on the network. Unlike other research, our method is aimed at classifying individual flows rather than just identifying P2P hosts or ports. Heuristics are analysed and a classification system proposed. The accuracy of the system is then tested using real network traffic from a core internet router showing over 99% accuracy in some cases. We expand on this proposed strategy to investigate its application to real-time, early classification problems. New proposals are made and the results of real-time experiments compared to those obtained in the data mining research. To the best of our knowledge this is the first research to use host based flow identification to determine a flows application within the early stages of the connection.

Structure of warm dense matter via angularly resolved x-ray scatter

In this paper we describe experimental results on angularly resolved x-ray scatter from a sample of warm dense aluminium that has been created by double sided laser-driven shock compression. The experiment was carried out on the Central Laser Facility of the Rutherford Appleton Laboratory, using the VULCAN laser. The form of the angularly resolved scatter cross-section was compared with predictions based on a series of molecular dynamics simulations with an embedded atom potential, a Yukakwa potential and a bare Coulomb potential. The importance of screening is evident from the comparison and the embedded atom model seems to match experiment better than the Yukawa potential.

Two-dimensional Banach spaces with polynomial numerical index zero

We study two-dimensional Banach spaces with polynomial numerical indices equal to zero.

Simultaneous remote haptic collaboration for assembling tasks

Stand-alone virtual environments (VEs) using haptic devices have proved useful for assembly/disassembly simulation of mechanical components. Nowadays, collaborative haptic virtual environments (CHVEs) are also emerging. A new peer-to-peer collaborative haptic assembly simulator (CHAS) has been developed whereby two users can simultaneously carry out assembly tasks using haptic devices. Two major challenges have been addressed: virtual scene synchronization (consistency) and the provision of a reliable and effective haptic feedback. A consistency-maintenance scheme has been designed to solve the challenge of achieving consistency. Results show that consistency is guaranteed. Furthermore, a force-smoothing algorithm has been developed which is shown to improve the quality of force feedback under adverse network conditions. A range of laboratory experiments and several real trials between Labein (Spain) and Queen’s University Belfast (Northern Ireland) have verified that CHAS can provide an adequate haptic interaction when both users perform remote assemblies (assembly of one user’s object with an object grasped by the other user). Moreover, when collisions between grasped objects occur (dependent collisions), the haptic feedback usually provides satisfactory haptic perception. Based on a qualitative study, it is shown that the haptic feedback obtained during remote assemblies with dependent collisions can continue to improve the sense of co-presence between users with regard to only visual feedback.

Classifying Network Protocols: A ‘2 Way’ Flow Approach

The identification and classification of network traffic and protocols is a vital step in many quality of service and security systems. Traffic classification strategies must evolve, alongside the protocols utilising the Internet, to overcome the use of ephemeral or masquerading port numbers and transport layer encryption. This research expands the concept of using machine learning on the initial statistics of flow of packets to determine its underlying protocol. Recognising the need for efficient training/retraining of a classifier and the requirement for fast classification, the authors investigate a new application of k-means clustering referred to as 'two-way' classification. The 'two-way' classification uniquely analyses a bidirectional flow as two unidirectional flows and is shown, through experiments on real network traffic, to improve classification accuracy by as much as 18% when measured against similar proposals. It achieves this accuracy while generating fewer clusters, that is, fewer comparisons are needed to classify a flow. A 'two-way' classification offers a new way to improve accuracy and efficiency of machine learning statistical classifiers while still maintaining the fast training times associated with the k-means.