Invariant ellipsoid method for minimization of unmatched disturbances effects in sliding mode control

A new sliding mode control design algorithm for a linear and a class of nonlinear quasi-Lipschitz disturbed system is presented. It is based on the appropriate selection of the sliding surface via invariant ellipsoid method. The designed control guarantees minimization of unmatched disturbances effects to system motions in a sliding mode. The obtained theoretical results are approved by numerical simulations.     

Large-scale linear system solver using secondary storage: Self-energy in hybrid nanostructures

We present a Fortran library which can be used to solve large-scale dense linear systems, Ax=b. The library is based on the LU decomposition included in the parallel linear algebra library PLAPACK and on its out-of-core extension POOCLAPACK. The library is complemented with a code which calculates the self-polarization charges and self-energy potential of axially symmetric nanostructures, following an induced charge computation method. Illustrative calculations are provided for hybrid semiconductor–quasi-metal zero-dimensional nanostructures. In these systems, the numerical integration of the self-polarization equations requires using a very fine mesh. This translates into very large and dense linear systems, which we solve for ranks up to 3×¹⁰⁵. It is shown that the self-energy potential on the semiconductor–metal interface has important effects on the electronic wavefunction.

Program summary

Program title: HDSS (Huge Dense System Solver)Catalogue identifier: AEHU_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHU_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 98 889No. of bytes in distributed program, including test data, etc.: 1 009 622Distribution format: tar.gzProgramming language: Fortran 90, CComputer: Parallel architectures: multiprocessors, computer clustersOperating system: Linux/UnixHas the code been vectorized or parallelized?: Yes. 4 processors used in the sample tests; tested from 1 to 288 processorsRAM: 2 GB for the sample tests; tested for up to 80 GBClassification: 7.3External routines: MPI, BLAS, PLAPACK, POOCLAPACK. PLAPACK and POOCLAPACK are included in the distribution file.Nature of problem: Huge scale dense systems of linear equations, Ax=B, beyond standard LAPACK capabilities. Application to calculations of self-energy potential in dielectrically mismatched semiconductor quantum dots.Solution method: The linear systems are solved by means of parallelized routines based on the LU factorization, using efficient secondary storage algorithms when the available main memory is insufficient. The self-energy solver relies on an induced charge computation method. The differential equation is discretized to yield linear systems of equations, which we then solve by calling the HDSS library.Restrictions: Simple precision. For the self-energy solver, axially symmetric systems must be considered.Running time: About 32 minutes to solve a system with approximately 100 000 equations and more than 6000 right-hand side vectors using a four-node commodity cluster with a total of 32 Intel cores.     

DOLAR: virtualizing heterogeneous information spaces to support their expansion

Users expect applications to successfully cope with the expansion of information as necessitated by the continuous inclusion of novel types of content. Given that such content may originate from ‘not‐seen thus far’ data collections and/or data sources, the challenging issue is to achieve the return of investment on existing services, adapting to new information without changing existing business‐logic implementation. To address this need, we introduce DOLAR (Data Object Language And Runtime), a service‐neutral framework which virtualizes the information space to avoid invasive, time‐consuming, and expensive source‐code extensions that frequently break applications. Specifically, DOLAR automates the introduction of new business‐logic objects in terms of the proposed virtual ‘content objects’. Such user‐specified virtual objects align to storage artifacts and help realize uniform ‘store‐to‐user’ data flows atop heterogeneous sources, while offering the reverse ‘user‐to‐store’ flows with identical effectiveness and ease of use. In addition, the suggested virtual object composition schemes help decouple business logic from any content origin, storage and/or structural details, allowing applications to support novel types of items without modifying their service provisions. We expect that content‐rich applications will benefit from our approach and demonstrate how DOLAR has assisted in the cost‐effective development and gradual expansion of a production‐quality digital library. Experimentation shows that our approach imposes minimal overheads and DOLAR‐based applications scale as well as any underlying datastore(s). Copyright © 2011 John Wiley & Sons, Ltd.     

Fully-adaptive algorithms for long-lived renaming

Long-lived renaming allows processes to repeatedly get distinct names from a small name space and release these names. This paper presents two long-lived renaming algorithms in which the name a process gets is bounded above by the number of processes currently occupying a name or performing one of these operations. The first algorithm is asynchronous, uses LL/SC objects, and has step complexity that is linear in the number of processes, c, currently getting or releasing a name. The second is synchronous, uses registers and counters, and has step complexity that is polylogarithmic in c. Both tolerate any number of process crashes.     

Example Based Everyting

The magic of computer graphics as seen in many current movies and computer games comes at a cost. Creating the geometric forms with which to generate synthetic characters and animating the characters to bring them to life require either highly skilled artists and/or sophisticated capture devices. Both are expensive and, in the case of highly skilled artists, rare.     

Automated discovery of local search heuristics for satisfiability testing

The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.     

A novel TFT‐OLED integration for OLED‐independent pixel programming in amorphous‐Si AMOLED pixels

Abstract— The direct voltage programming of active‐matrix organic light‐emitting‐diode (AMOLED) pixels with n‐channel amorphous‐Si (a‐Si) TFTs requires a contact between the driving TFT and the OLED cathode. Current processing constraints only permit connecting the driving TFT to the OLED anode. Here, a new “inverted” integration technique which makes the direct programming possible by connecting the driver n‐channel a‐Si TFT to the OLED cathode is demonstrated. As a result, the pixel drive current increases by an order of magnitude for the same data voltages and the pixel data voltage for turn‐on drops by several volts. In addition, the pixel drive current becomes independent of the OLED characteristics so that OLED aging does not affect the pixel current. Furthermore, the new integration technique is modified to allow substrate rotation during OLED evaporation to improve the pixel yield and uniformity. The new integration technique is important for realizing active‐matrix OLED displays with a‐Si technology and conventional bottom‐anode OLEDs.     

Evolvable agents,a fine grained approach for distributed evolutionary computing: walking towards the peer-to-peer computing frontiers

In this work we propose a fine grained approach with self-adaptive migration rate for distributed evolutionary computation. Our target is to gain some insights on the effects caused by communication when the algorithm scales. To this end, we consider a set of basic topologies in order to avoid the overlapping of algorithmic effects between communication and topological structures. We analyse the approach viability by comparing how solution quality and algorithm speed change when the number of processors increases and compare it with an Island model based implementation. A finer-grained approach implies a better chance of achieving a larger scalable system; such a feature is crucial concerning large-scale parallel architectures such as peer-to-peer systems. In order to check scalability, we perform a threefold experimental evaluation of this model: first, we concentrate on the algorithmic results when the problem scales up to eight nodes in comparison with how it does following the Island model. Second, we analyse the computing time speedup of the approach while scaling. Finally, we analyse the network performance with the proposed self-adaptive migration rate policy that depends on the link latency and bandwidth. With this experimental setup, our approach shows better scalability than the Island model and a equivalent robustness on the average of the three test functions under study.     

Optimal neuronal tuning for finite stimulus spaces

The efficiency of neuronal encoding in sensory and motor systems has been proposed as a first principle governing response properties within the central nervous system. We present a continuation of a theoretical study presented by Zhang and Sejnowski, where the influence of neuronal tuning properties on encoding accuracy is analyzed using information theory. When a finite stimulus space is considered, we show that the encoding accuracy improves with narrow tuning for one- and two-dimensional stimuli. For three dimensions and higher, there is an optimal tuning width.