Denial of service attacks and challenges in broadband wireless networks

Broadband wireless networks are providing internet and related services to end users. The three most important broadband wireless technologies are IEEE 802.11, IEEE 802.16, and Wireless Mesh Network (WMN). Security attacks and vulnerabilities vary amongst these broadband wireless networks because of differences in topologies, network operations and physical setups. Amongst the various security risks, Denial of Service (DoS) attack is the most severe security threat, as DoS can compromise the availability and integrity of broadband wireless network. In this paper, we present DoS attack issues in broadband wireless networks, along with possible defenses and future directions

Towards obtaining robust boundary condition parameters to aid accuracy in FEA thermal error predictions

Finite Element Analysis (FEA) is used as a design tool within engineering industries due to the capability for rapid summative analysis accompanied by the visual aid. However, to represent realistic behaviour, FEA relies heavily on input parameters which must ideally be based on true figures such as data from experimental testing which sometimes requires time-consuming testing regimes. In the case of machine tool assemblies where complex structural joints and linkages are present, access to those areas can be a primary constraint to obtaining related boundary parameters such as heat flow across joints, for which, assumptions are incorporated to the FEA model which in effect increase the uncertainty in the FEA predictions. Similarly, in the case of thermal error modelling, simplifications are made when representing thermal boundary conditions such as the application of a uniform convection parameter to an assembly with parts assembled in both horizontal and vertical orientations. This research work aims to reduce the number of assumptions by providing experimentally obtained thermal boundary condition parameters. This work acknowledges experimental regimes that focus on obtaining thermal parameters related to the conduction across assembly joints (Thermal Contact Conductance-TCC) and measures the convection around areas such as belt drives and rotating parts to obtain convection parameters as inputs to the FEA. It provides TCC parameters for variable interfacial behaviour based on the varying contact pressure and the heat flow through dry and oiled contacts such as the conduction from spindle bearings to the surrounding housing and conduction from guideways into the associated assembly through carriages and contact bearings. It provides convection parameters across the test mandrel rotating at different speeds and around stationary structures such as convection parameters observed during TCC tests. It also provide details on the methods used to obtain all these parameters such as the use of thermal imaging, sensors placements and methods to obtain these boundary condition parameters. The significance of this work is to improve dramatically FEA thermal predictions, which are a critical part of engineering design. Although the focus is on machine tool design, the process and parameters can equally be applied to other areas of thermodynamic behaviour

Efficient estimation by FEA of machine tool distortion due to environmental temperature perturbations

Machine tools are susceptible to exogenous influences, which mainly derive from varying environmental conditions such as the day and night or seasonal transitions during which large temperature swings can occur. Thermal gradients cause heat to flow through the machine structure and results in non-linear structural deformation whether the machine is in operation or in a static mode. These environmentally stimulated deformations combine with the effects of any internally generated heat and can result in significant error increase if a machine tool is operated for long term regimes. In most engineering industries, environmental testing is often avoided due to the associated extensive machine downtime required to map empirically the thermal relationship and the associated cost to production. This paper presents a novel offline thermal error modelling methodology using finite element analysis (FEA) which significantly reduces the machine downtime required to establish the thermal response. It also describes the strategies required to calibrate the model using efficient on-machine measurement strategies. The technique is to create an FEA model of the machine followed by the application of the proposed methodology in which initial thermal states of the real machine and the simulated machine model are matched. An added benefit is that the method determines the minimum experimental testing time required on a machine; production management is then fully informed of the cost-to-production of establishing this important accuracy parameter. The most significant contribution of this work is presented in a typical case study; thermal model calibration is reduced from a fortnight to a few hours. The validation work has been carried out over a period of over a year to establish robustness to overall seasonal changes and the distinctly different daily changes at varying times of year. Samples of this data are presented that show that the FEA-based method correlated well with the experimental results resulting in the residual errors of less than 12 μm

Application of multi sensor data fusion based on Principal Component Analysis and Artificial Neural Network for machine tool thermal monitoring

Due to the various heat sources on a machine tool, there exists a complex temperature distribution across its structure. This causes an inherent thermal hysteresis which is undesirable as it affects the systematic tool –to-workpiece positioning capability. To monitor this, two physical quantities (temperature and strain) are measured at multiple locations. This article is concerned with the use of Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) to fuse this potentially large amount of data from multiple sources. PCA reduces the dimensionality of the data and thus reduces training time for the ANN which is being used for thermal modelling. This paper shows the effect of different levels of data compression and the application of rate of change of sensor values to reduce the effect of system hysteresis. This methodology has been successfully applied to the ram of a 5-axis gantry machine with 90 % correlation to the measured displacement

FEA-based design study for optimising non-rigid error detection on machine tools

Non-rigid-body behaviour can have a considerable effect on the overall accuracy performance of machine tools. These errors originate from bending of the machine structure due to change in distribution of its own weight or from movement of the workpiece and fixture. These effects should be reduced by good mechanical design, but residual errors can still be problematic due to realistic material and cost limitations. One method of compensation is to measure the deformation directly with sensors embedded in a metrology frame. This paper presents an FEA-based design study which assesses finite stiffness effects in both the machine structure and its foundation to optimise the sensitivity of the frame to the resulting errors. The study results show how a reference artefact, optimised by the FEA study, can be used to detect the distortion

Thermal Error Modelling of a CNC Machine Tool Feed Drive System using FEA Method

Recirculating ball screw systems are commonly used in machine tools and are one of the major heat sources which cause considerable thermal drift in CNC machine tools. Finite Element Analysis (FEA) method has been used successfully in the past to model the thermal characteristics of machine tools with promising results. Since FEA predictions are highly dependent on the efficacy of numerical parameters including the surrounding Boundary Conditions (BC), this study emphasises on an efficient modelling method to obtain optimised numerical parameters for acquiring a qualitative response from the feed drive system model. This study was performed on a medium size Vertical Machining Centre (VMC) feed drive system in which two parameter dentification methods have been employed; the general prediction method based on formulae provided by OEMs, and the energy balance method. The parameters obtained from both methods were applied to the FEA model of the machine feed drive system and validated against experimental results. Correlation with which was increased from 70 % to 80 % using the energy balance method

Electrical transport and optical studies of ferromagnetic Cobalt doped ZnO nanoparticles exhibiting a metal-insulator transition

The observed correlation of oxygen vacancies and room temperature ferromagnetic ordering in Co doped ZnO1-o nanoparticles reported earlier (Naeem et al Nanotechnology 17, 2675-2680) has been further explored by transport and optical measurements. In these particles room temperature ferromagnetic ordering had been observed to occur only after annealing in forming gas. In the current work the optical properties have been studied by diffuse reflection spectroscopy in the UV-Vis region and the band gap of the Co doped compositions has been found to decrease with Co addition. Reflections minima are observed at the energies characteristic of Co+2 d-d (tethrahedral symmetry) crystal field transitions, further establishing the presence of Co in substitutional sites. Electrical transport measurements on palletized samples of the nanoparticles show that the effect of a forming gas is to strongly decrease the resistivity with increasing Co concentration. For the air annealed and non-ferromagnetic samples the variation in the resistivity as a function of Co content are opposite to those observed in the particles prepared in forming gas. The ferromagnetic samples exhibit an apparent change from insulator to metal with increasing temperatures for T>380K and this change becomes more pronounced with increasing Co content. The magnetic and resistive behaviors are correlated by considering the model by Calderon et al [M. J. Calderon and S. D. Sarma, Annals of Physics 2007 (Accepted doi: 10.1016/j.aop.2007.01.010] where the ferromagnetism changes from being mediated by polarons in the low temperature insulating region to being mediated by the carriers released from the weakly bound states in the higher temperature metallic region.Comment: 7 pages, 6 figure

Semi-leptonic Ds+D_s^+(1968) decays as a scalar meson probe

The unusual multiplet structures associated with the light spin zero mesons have recently attracted a good deal of theoretical attention. Here we discuss some aspects associated with the possibility of getting new experimental information on this topic from semi-leptonic decays of heavy charged mesons into an isosinglet scalar or pseudoscalar plus leptons.Comment: 11 pages, 4 figure

Semantic-Based Process Mining Technique for Annotation and Modelling of Domain Processes

Semantic technologies aim to represent information or models in formatsthat are not just machine-readable but also machine-understandable. To this effect, thispaper shows how the semantic concepts can be layered on top of the derived models toprovide a more contextual analysis of the models through the conceptualization method.Technically, the method involves augmentation of informative value of the resulting mod-els by semantically annotating the process elements with concepts that they represent inreal-time settings, and then linking them to an ontology in order to allow for a moreabstract analysis of the extracted logs or models. The work illustrates the method usingthe case study of a learning process domain. Consequently, the results show that a systemwhich is formally encoded with semantic labelling (annotation), semantic representation(ontology) and semantic reasoning (reasoner) has the capacity to lift the process miningand analysis from the syntactic to a more conceptual level