MIMO Wiener model identification for large scale fading of wireless mobile communications links

The large scale fading of wireless mobile communications links is modelled assuming the mobile receiver motion is described by a dynamic linear system in state-space. The geometric relations involved in the attenuation and multi-path propagation of the electric field are described by a static non-linear mapping. A Wiener system subspace identification algorithm in conjunction with polynomial regression is used to identify a model from time-domain estimates of the field intensity assuming a multitude of emitters and an antenna array at the receiver end.

The impact of genome defense on mobile elements in Microbotryum

Repeat induced point mutation (RIP), a mechanism causing hypermutation of repetitive DNA sequences in fungi, has been described as a ‘genome defense’ which functions to inactivate mobile elements and inhibit their deleterious effects on genome stability. Here we address the interactions between RIP and transposable elements in the Microbotryum violaceum species complex. Ten strains of M. violaceum, most of which belong to different species of the fungus, were all found to contain intragenomic populations of copia-like retrotransposons. Intragenomic DNA sequence variation among the copia-like elements was analyzed for evidence of RIP. Among species with RIP, there was no significant correlation between the frequency of RIP-induced mutations and inferred transposition rate based on diversity. Two strains of M. violaceum, from two different plant species but belonging to the same fungal lineage, contained copia-like elements with very low diversity, as would result from a high transposition rate, and these were also unique in showing no evidence of the hypermutation patterns indicative of the RIP genome defense. In this species, evidence of RIP was also absent from a Class II helitron-like transposable element. However, unexpectedly the absolute repetitive element load was lower than in other strains.

Distributed acceleration of mobile radio network optimisation algorithms

The past decade has witnessed explosive growth of mobile subscribers and services. With the purpose of providing better-swifter-cheaper services, radio network optimisation plays a crucial role but faces enormous challenges. The concept of Dynamic Network Optimisation (DNO), therefore, has been introduced to optimally and continuously adjust network configurations, in response to changes in network conditions and traffic. However, the realization of DNO has been seriously hindered by the bottleneck of optimisation speed performance. An advanced distributed parallel solution is presented in this paper, as to bridge the gap by accelerating the sophisticated proprietary network optimisation algorithm, while maintaining the optimisation quality and numerical consistency. The ariesoACP product from Arieso Ltd serves as the main platform for acceleration. This solution has been prototyped, implemented and tested. Real-project based results exhibit a high scalability and substantial acceleration at an average speed-up of 2.5, 4.9 and 6.1 on a distributed 5-core, 9-core and 16-core system, respectively. This significantly outperforms other parallel solutions such as multi-threading. Furthermore, augmented optimisation outcome, alongside high correctness and self-consistency, have also been fulfilled. Overall, this is a breakthrough towards the realization of DNO.

Neural network control of a simple mobile robot

In recent years researchers in the Department of Cybernetics have been developing simple mobile robots capable of exploring their environment on the basis of the information obtained from a few simple sensors. These robots are used as the test bed for exploring various behaviours of single and multiple organisms: the work is inspired by considerations of natural systems. In this paper we concentrate on that part of the work which involves neural networks and related techniques. These neural networks are used both to process the sensor information and to develop the strategy used to control the robot. Here the robots, their sensors, and the neural networks used and all described. 1.

Joint modelling of obstacle induced and mesoscale changes: current limits and challenges

Obstacles considerably influence boundary layer processes. Their influences have been included in mesoscale models (MeM) for a long time. Methods used to parameterise obstacle effects in a MeM are summarised in this paper using results of the mesoscale model METRAS as examples. Besides the parameterisation of obstacle influences it is also possible to use a joint modelling approach to describe obstacle induced and mesoscale changes. Three different methods may be used for joint modelling approaches: The first method is a time-slice approach, where steady basic state profiles are used in an obstacle resolving microscale model (MiM, example model MITRAS) and diurnal cycles are derived by joining steady-state MITRAS results. The second joint modelling approach is one-way nesting, where the MeM results are used to initialise the MiM and to drive the boundary values of the MiM dependent on time. The third joint modelling approach is to apply multi-scale models or two-way nesting approaches, which include feedbacks from the MiM to the MeM. The advantages and disadvantages of the different approaches and remaining problems with joint Reynolds-averaged Navier–Stokes modelling approaches are summarised in the paper.

Virtual reshaping and invisibility in obstacle scattering

We consider reshaping an obstacle virtually by using transformation optics in acoustic and electromagnetic scattering. Among the general virtual reshaping results, the virtual minification and virtual magnification in particular are studied. Stability estimates are derived for scattering amplitude in terms of the diameter of a small obstacle, which implies that the limiting case for minification corresponds to a perfect cloaking, i.e., the obstacle is invisible to detection.