Multicellular Alamouti scheme performance in Rayleigh and shadow fading

In this paper, we study the performance of two downlink multicellular systems: a multiple inputs single output (MISO) system using the Alamouti code and a multiple inputs multiple outputs (MIMO) system using the Alamouti code at the transmitter side and a maximum ratio combining (MRC) as a receiver, in terms of outage probability. The channel model includes path-loss, shadowing, and fast fading, and the system is considered interference-limited. Two cases are distinguished: constant shadowing and log-normally distributed shadowing. In the first case, closed form expressions of the outage probability are proposed. For a log-normally distributed shadowing, we derive easily computable expressions of the outage probability. The proposed expressions allow for fast and simple performance evaluation for the two multicellular wireless systems: MISO Alamouti and MIMO Alamouti with MRC receiver. We use a fluid model approach to provide simpler outage probability expressions depending only on the distance between the considered user and its serving base station.     

Evolution of solute clustering in Al–Cu–Mg alloys during secondary ageing

The evolution of an atomistic-level nanostructure during the early stages of secondary ageing of a rapid hardening Al–1.1Cu–1.7Mg (at.%) alloy has been characterized by a combination of atom probe tomography (APT) and transmission electron microscopy. Quantitative APT analysis reveals key changes in the evolution of solute clusters during secondary ageing (T6I4 condition) that correlate with secondary hardness increments. The microstructures that cause peak hardness differ between the T6 and T6I4 tempers – the former is a result of solute clustering as well as the precipitation of GPB zones and S phase, whereas in the latter, secondary ageing promotes only the formation of solute clusters. Cu–Mg clusters with high Mg:Cu ratio have the most strengthening potency during secondary ageing in T6I4 heat treatment.     

A Generic Framework for a Combined Agent-based Market and Production Model

Agent-based market models are in general based on a-priori defined supply and demand schemes. Likewise, production models assume that prices are known a-priori. In reality prices depend on variable demands and supplies, while demand and supply depend on variable prices, and these two processes are interconnected. This paper describes a model that for the first time simulates a combined agent-based double auction market and production model. The model is built around von Neumann technology matrices (von Neumann, Rev Econ Stud 13(1):1–9, 1946) which provide the links between products. Agents possess one or more technologies to produce products from other products. They trade in order to acquire the inputs and sell in order to generate revenue, and the price is determined by a process of negotiation between buyers and sellers. The algorithm of negotiation is based on Cliff’s Zero Intelligence Plus approach (Cliff, Minimal-intelligence agents for bargaining behaviors in market-based environments. Technical report, School of Cognitive and Computing Sciences, University of Sussex, 1997), but instead of a single commodity with fixed limit prices the agents change their limit prices for multiple products based on the simulated economic situation. The combination of production and market provides a simple but complete bottom-up model framework for microeconomics. As the results show, the model employs a price mechanism that results in an appropriate allocation of resources without a central command.     

S-Space: A new concept for information extraction from imaging spectrometer data

Imaging spectroscopy records the solar reflected spectrum at a fine spectral resolution and in a large number of bands thereby producing a spectral profile associated with each pixel in an image. This type of data tends to be highly correlated and we intend to harness the information of this spectral dependence by introducing the S-space concept. This concept in conjunction with measures of spatial dependence allows one to visualize the spectral profile as a regionalized variable where distance is measured in wavelengths. Unlike image space, S-space is one-dimensional. We illustrate the S-space concept using a CASI image of a forest scene and an AVIRIS image of an urban scene. This new technique provides spectral correlation information for each individual spectral profile on a per-pixel basis rather than the spectral variability across the entire image as is traditionally done in remote sensing investigations. As an example of the possibilities, spectral dependence was quantified using the semivariogram in S-space. A model of spatial dependence was then fitted to each semivariogram and the model parameters used as input to a classification algorithm in order to extract land cover information. To compare our approach with standard techniques, we used the first three principal components to produce a land cover classification. The semivariogram model parameter derived classification results displayed a better spatial contiguity and greatly diminished the dimensionality of the dataset. We also discuss future directions for the use of the S-space concept.     

A Web-based virtual environment for operator training [for powersystems]

The wide accessibility and low cost of virtual reality (VR) on the World Wide Web with the advent of the Virtual Reality Modelling Language (VRML) and VRML interpreters makes it ideal for use in providing cost-effective training for operators in selected complex technical environments. This paper presents SEDA-VRML, a VR-based learn-by-doing system developed for the training of maintenance workers in the troubleshooting and inspection of power transformers at Hydro-Quebec that is accessible from a Web browser. SEDA-VRML is a prototype application of a reuse-driven design incorporating VRML, Java and an expert system within a user interface system (UIS) architecture. The paper describes the functionality and design principles of SEDA-VRML, with key implementation results, to show how SEDA-VRML's design can indeed answer to the limitations of VRML to act as a cost-effective alternative to traditional operator training systems for operator training in VR, as well as a basis for the rapid development of future operator training systems     

SIR Estimation in Hexagonal Cellular Networks with Best Server Policy

The evaluation of the Signal to Interference Ratio (SIR) in cellular networks is of primary importance for network dimensioning. For static studies, which evaluate cell capacity and coverage, as well as for dynamic studies, which consider arrivals and departures of mobile stations (MS), the SIR is always an important input. Contrary to most of the analytical works evaluating SIR, we assume in this paper that the MS is attached to the best server, i.e., to the base station (BS) from which it receives the highest power. This is a more realistic policy compared to the classical one that considers MSs to be attached to the nearest BS. The exact formulation of the SIR is however in this case uneasy to handle and numerical methods remain heavy. In this paper, we thus propose an approximate analytical study on the average SIR and SIR distribution in lognormally shadowed networks based on truncated lognormal distributions that provides very close results with respect to Monte Carlo simulations.     

Identifying dominant factors for the calibration of a land-use cellular automata model using Rough Set Theory

While cellular automata (CA) are increasingly used for modeling urban growth and land-use changes, the methods for identifying the dominant factors that drive the landscape dynamics when calibrating the model still require improvement, specifically in the context where a large number of factors are considered. In this paper, the potential of Rough Set Theory (RST) to guide the factor selection is evaluated. This data mining approach was tested for the calibration of a CA model to simulate land-use changes in a portion of the Elbow River watershed adjacent to the City of Calgary, in southern Alberta, Canada. Simulation outcomes obtained using a total of 18 original factors and a smaller set of factors identified with RST were compared to reference land-use maps using three Kappa coefficients of agreement. Results reveal that the factors selected by RST are not identical for each land use. Among the identified factors, three external factors (distance to river, distance to Calgary City center, and distance to road) and the presence of Built-up areas in the three considered neighborhoods are the most important factors driving the transition from Forest and Vegetation (including agriculture and Rangeland/Parkland) to Built-up. The Kappa statistics reveal that the factors selected by RST tend to generate a higher agreement with reference land-use maps than the original group of 18 factors and that they are better at capturing quantity information than location information. An advantage of RST is that it retains the original factors in the identification of the transition rules. In addition, the computation time required for the simulation using the RST factors is considerably less than the time needed to generate the results using the original set of factors. However, the data mining technique itself is computationally intensive. This study illustrates that RST can guide the selection of the dominant factors required in the calibration of a CA model, but that its potential still needs to be further investigated.     

Role of nanostructure in pitting of Al-Cu-Mg alloys

Aluminium alloys possess a unique combination of strength and low density, which historically have made these alloys ideal for many structural applications. However, addition of alloying elements in appreciable concentrations, in particular Cu and Mg, make Al-alloys inherently susceptible to localised corrosion. In this work, we adopt a bottom-up approach in an attempt to quantify the critical microstructural feature size (viz. precipitate size) that is capable of triggering a cascade of pitting events and eventual degradation of corrosion resistance. This is accomplished by exploiting the well characterised hardening response in a model alloy, Al-1.1Cu-1.7Mg (at.%), for which pitting resistance of the alloy was tracked with aging time and hence microstructural evolution. Corrosion performance and microstructural characterisation were carried out using a combination of electrochemical testing, coupled with high-resolution scanning transmission electron microscopy (HRSTEM) and atom probe tomography (APT). Results indicate, at least for this particular alloy, that second phase features below a critical width of approximately 3 nm can be tolerated from a corrosion perspective. This study has potentially wide consequences in the understanding of aluminium alloy corrosion initiation and the development of highly corrosion resistant aluminium alloys.