MMORPG Player actions: Network performance, session patterns and latency requirements analysis

Providing Massively Multiplayer Online Role-Playing Games (MMORPGs) is a big challenge for future mobile, IP-based networks. Understanding how the players’ actions affect the network parameters, the game platform, and the overall perceived quality is highly relevant for the purposes of game design, as well as for the networking infrastructure and network support for games. We break player actions down into discrete categories, and show that each category is distinct in terms of several key metrics. We discuss which categories of actions could be supported on current mobile devices, and present evidence in form of a user survey demonstrating the demand for such services. The starting points into the discussion include the networking, session and latency requirements for particular player actions on one side, and the players’ interest on the other. The Blizzard Entertainment’s World of Warcraft (WoW) is used as a case study.     

Aerodynamic design and analysis of a small-scale vertical axis wind turbine

Small-scale vertical axis wind turbines are regarded today as an attractive source of green energy, still insufficiently implemented and tested. This paper presents a fast design methodology of such a VAWT, in terms of choosing its main parameters: airfoil, rotor diameter and solidity. For obtaining generated power of each considered geometrical model at different undisturbed wind velocities and rotor angular velocities two models were used — momentum and vortex-wake model, combined with experimentally measured airfoil data (airfoil lift and drag coefficients). Even though the former model is simpler, it is the most utilized model, known to provide good results in stationary working regimes. Both models still present fairly accurate and fast tools for computation and optimization, particularly useful in the phase of conceptual design. In this research, the use of the momentum model resulted in determination of the maximal power coefficient, optimal- and minimal freestream velocity for every considered VAWT model. From these output parameters, a selection of the optimal geometric model was done, and a more detailed transient analysis and flow representation around the selected solution was obtained by the vortex-wake model. The results obtained by the two used computational models coincide satisfactorily.     

Boosted manifold principal angles for image set-based recognition

In this paper we address the problem of classifying vector sets. We motivate and introduce a novel method based on comparisons between corresponding vector subspaces. In particular, there are two main areas of novelty: (i) we extend the concept of principal angles between linear subspaces to manifolds with arbitrary nonlinearities; (ii) it is demonstrated how boosting can be used for application-optimal principal angle fusion. The strengths of the proposed method are empirically demonstrated on the task of automatic face recognition (AFR), in which it is shown to outperform state-of-the-art methods in the literature.     

Minimising conservatism in infinite-horizon LQR control

This paper studies the formulation of the constrained infinite horizon linear quadratic regulator control law (CIHLQR). Results from recent studies in this area are extended to show that conditions used in the standard formulation of the CIHLQR law are not necessary, but merely sufficient. Through the use of a novel proof it is shown that for a general SISO system with input constraints and certain conditions imposed, saturated LQR provides the same control sequence as CIHLQR. It is further shown that saturated LQR is equivalent to the CIHLQR in the case of first-order systems, subject to both state and control constraints. Finally, the region of constrained stabilisability is characterised for the case of open-loop unstable first-order systems.     

Lake Level Prediction using Feed Forward and Recurrent Neural Networks

The protection of high quality fresh water in times of global climate changes is of tremendous importance since it is the key factor of local demographic and economic development. One such fresh water source is Vrana Lake, located on the completely karstified Island of Cres in Croatia. Over the last few decades a severe and dangerous decrease of the lake level has been documented. In order to develop a reliable lake level prediction, the application of the artificial neural networks (ANN) was used for the first time. The paper proposes time-series forecasting models based on the monthly measurements of the lake level during the last 38 years, capable to predict 6 or 12 months ahead. In order to gain the best possible model performance, the forecasting models were built using two types of ANN: the Long-Short Term Memory (LSTM) recurrent neural network (RNN), and the feed forward neural network (FFNN). Instead of classic lagged data set, the proposed models were trained with the set of sequences with different length created from the time series data. The models were trained with the same set of the training parameters in order to establish the same conditions for the performance analysis. Based on root mean squared error (RMSE) and correlation coefficient (R) the performance analysis shown that both model types can achieve satisfactory results. The analysis also revealed that regardless of the model types, they outperform classic ANN models based on datasets with fixed number of features and one month the prediction period. Analysis also revealed that the proposed models outperform classic time series forecasting models based on ARIMA and other similar methods .

     

Multivariate statistical analysis of Raman images of a pharmaceutical tablet

This paper describes the application of principal component analysis (PCA) and independent component analysis (ICA) to identify the reference spectra of a pharmaceutical tablet's constituent compounds from Raman spectroscopic data. The analysis shows, first with a simulated data set and then with data collected from a pharmaceutical tablet, that both PCA and ICA are able to identify most of the features present in the reference spectra of the constituent compounds. However, the results suggest that the ICA method may be more appropriate when attempting to identify unknown reference spectra from a sample. The resulting PCA and ICA models are subsequently used to estimate the relative concentrations of the constituent compounds and to produce spatial distribution images of the analyzed tablet. These images provide a visual representation of the spatial distribution of the constituent compounds throughout the tablet. Images associated with the ICA scores are found to be more informative and not as affected by measurement noise as the PCA based score images. The paper concludes with a discussion of the future work that needs to be undertaken for ICA to gain wider acceptance in the applied spectroscopy community.     

Computationally efficient application of the generic shape-illumination invariant to face recognition from video

As a problem of high practical appeal but many outstanding challenges, computer-based face recognition remains a topic of extensive research attention. In this paper we are specifically interested in the task of identifying a person using multiple images both in training and as a query. Thus, a novel method is proposed which advances the state-of-the-art in set-based face recognition. The introduced approach is based on a previously described invariant in the form of generic shape-illumination effects. The contributions include (i) an analysis of the computational demands of the original method and a demonstration of its practical limitations, (ii) a novel representation of personal appearance in the form of linked mixture models in image and pose-signature spaces, and (iii) an efficient (in terms of storage needs and matching time) manifold re-illumination algorithm based on the aforementioned representation. An evaluation and comparison of the proposed method with the original generic shape-illumination algorithm shows that comparably high recognition rates are achieved on a large data set (1.5% error on 700 face sets containing 100 individuals and extreme illumination variation) with a dramatic improvement in matching speed (over 700 times for sets containing 1600 faces) and storage requirements (independent of the number of training images). Theoretical and empirical findings of the present work are used to identify and discuss avenues for future research.     

Isogeometric bending analysis of composite plates based on a higher-order shear deformation theory

This research paper presents an isogeometric plate finite element formulation for analysis of thick composite plates. Isogeometric finite element method which is based on non-uniform rational B-splines (NURBS) basis functions, is a novel numerical procedure developed to bridge the gap between CAD and FEM modeling of structures. In order to investigate the behavior of isogeometric plate elements under static loading, plate kinematics is based on third order shear deformation theory (TSDT) of Reddy, which is free from transverse shear locking. This paper discusses accurate transverse stress recovery procedures for TSDT isogeometric finite elements. Numerical experiments with quadratic, cubic and quartic elements are presented and obtained results are compared to other available ones.