Cooperative Caching Framework for Mobile Cloud Computing

Due to the advancement in mobile devices and wireless networks mobile cloud computing, which combines mobile computing and cloud computing has gained momentum since 2009. The characteristics of mobile devices and wireless network makes the implementation of mobile cloud computing more complicated than for fixed clouds. This section lists some of the major issues in Mobile Cloud Computing. One of the key issues in mobile cloud computing is the end to end delay in servicing a request. Data caching is one of the techniques widely used in wired and wireless networks to improve data access efficiency. In this paper we explore the possibility of a cooperative caching approach to enhance data access efficiency in mobile cloud computing. The proposed approach is based on cloudlets, one of the architecture designed for mobile cloud computing.

Computing median and antimedian sets in median graphs

The median (antimedian) set of a profile π = (u1, . . . , uk) of vertices of a graphG is the set of vertices x that minimize (maximize) the remoteness i d(x,ui ). Two algorithms for median graphs G of complexity O(nidim(G)) are designed, where n is the order and idim(G) the isometric dimension of G. The first algorithm computes median sets of profiles and will be in practice often faster than the other algorithm which in addition computes antimedian sets and remoteness functions and works in all partial cubes

ECG Noise Removal using GA tuned Sign-Data Least Mean Square Algorithm

Adaptive filter is a primary method to filter Electrocardiogram (ECG), because it does not need the signal statistical characteristics. In this paper, an adaptive filtering technique for denoising the ECG based on Genetic Algorithm (GA) tuned Sign-Data Least Mean Square (SD-LMS) algorithm is proposed. This technique minimizes the mean-squared error between the primary input, which is a noisy ECG, and a reference input which can be either noise that is correlated in some way with the noise in the primary input or a signal that is correlated only with ECG in the primary input. Noise is used as the reference signal in this work. The algorithm was applied to the records from the MIT -BIH Arrhythmia database for removing the baseline wander and 60Hz power line interference. The proposed algorithm gave an average signal to noise ratio improvement of 10.75 dB for baseline wander and 24.26 dB for power line interference which is better than the previous reported works

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.

Rezentralisierung: Server-Based Computing in der Praxis einer Universitätsbibliothek

Der Beitrag beschreibt die Ein- und Durchführung einer Server-basierten Computerinfrastruktur in einer Universitätsbibliothek. Beschrieben wird das so genannte MetaFrame-DV-Konzept der Universitätsbibliothek Kassel, das das dortige Informationsmanagement in den letzten vier Jahren initiiert, konzipiert und umgesetzt hat. Hierbei werden nunmehr nicht mehr nur Applikationsserver z.B. für das CD-Angebot eingesetzt, sondern sämtliche ca. 200 Mitarbeiter- und Funktionsarbeitsplätze über eine Citrix MetaFrame-Installation serverseitig betreut. Besonderes Augenmerk gilt in diesem Beitrag der Konfiguration, der praktischen Administration und den täglichen Arbeitsbedingungen an den Bibliotheksmitarbeiterarbeitsplätzen.

Micro-electromechanical structural design and optimization of vertical cavity photonic devices with wide continuous tuning

Diese Arbeit umfaßt das elektromechanische Design und die Designoptimierung von weit durchstimmbaren optischen multimembranbasierten Bauelementen, mit vertikal orientierten Kavitäten, basierend auf der Finiten Element Methode (FEM). Ein multimembran InP/Luft Fabry-Pérot optischer Filter wird dargestellt und umfassend analysiert. In dieser Arbeit wird ein systematisches strukturelles Designverfahren dargestellt. Genaue analytische elektromechanischer Modelle für die Bauelemente sind abgeleitet worden. Diese können unschätzbare Werkzeuge sein, um am Anfang der Designphase schnell einen klaren Einblick zur Verfügung zu stellen. Mittels des FEM Programms ist der durch die nicht-lineare Verspannung hervorgerufene versteifende Effekt nachgeforscht und sein Effekt auf die Verlängerung der mechanischen Durchstimmungsstrecke der Bauelemente demonstriert worden. Interessant war auch die Beobachtung, dass die normierte Relation zwischen Ablenkung und Spannung ein unveränderliches Profil hat. Die Deformation der Membranflächen der in dieser Arbeit dargestellten Bauelementformen erwies sich als ein unerwünschter, jedoch manchmal unvermeidbarer Effekt. Es zeigt sich aber, dass die Wahl der Größe der strukturellen Dimensionen den Grad der Membrandeformation im Falle der Aktuation beeinflusst. Diese Arbeit stellt ein elektromechanisches in FEMLAB implementierte quasi-3D Modell, das allgemein für die Modellierung dünner Strukturen angewendet werden kann, dar; und zwar indem man diese als 2D-Objekte betrachtet und die dritte Dimension als eine konstante Größe (z.B. die Schichtdicke) oder eine Größe, welche eine mathematische Funktion ist, annimmt. Diese Annahme verringert drastisch die Berechnungszeit sowie den erforderlichen Arbeitsspeicherbedarf. Weiter ist es für die Nachforschung des Effekts der Skalierung der durchstimmbaren Bauelemente verwendet worden. Eine neuartige Skalierungstechnik wurde abgeleitet und verwendet. Die Ergebnisse belegen, dass das daraus resultierende, skalierte Bauelement fast genau die gleiche mechanische Durchstimmung wie das unskalierte zeigt. Die Einbeziehung des Einflusses von axialen Verspannungen und Gradientenverspannungen in die Berechnungen erforderte die Änderung der Standardimplementierung des 3D Mechanikberechnungsmodus, der mit der benutzten FEM Software geliefert wurde. Die Ergebnisse dieser Studie zeigen einen großen Einfluss der Verspannung auf die Durchstimmungseigenschaften der untersuchten Bauelemente. Ferner stimmten die Ergebnisse der theoretischen Modellrechnung mit den experimentellen Resultaten sehr gut überein.

Überwachung der Vorspannkraft Externer Spannglieder mit Hilfe der Modalanalyse

Aufgrund ihrer Vorteile hinsichtlich Dauerhaftigkeit und Bauwerkssicherheit ist in Deutschland seit 1998 die externe Vorspannung in Hohlkastenbrücken zur Regelbauweise geworden. Durch Verwendung der austauschbaren externen Vorspannung verspricht man sich im Brückenbau weitere Verbesserungen der Robustheit und damit eine Verlängerung der Lebensdauer. Trotz des besseren Korrosionsschutzes im Vergleich zur internen Vorspannung mit Verbund sind Schäden nicht völlig auszuschließen. Um die Vorteile der externen Vorspannung zu nutzen, ist daher eine periodische Überwachung der Spanngliedkräfte, z. B. während der Hauptprüfung des Bauwerks, durchzuführen. Für die Überwachung der Spanngliedkräfte bei Schrägseilbrücken haben sich die Schwingungsmessmethoden als wirtschaftlich und leistungsfähig erwiesen. Für die Übertragung der Methode auf den Fall der externen Vorspannung, wo kürzere Schwingungslängen vorliegen, waren zusätzliche Untersuchungen hinsichtlich der effektiven Schwingungslänge, der Randbedingungen sowie der effektiven Biegesteifigkeit erforderlich. Im Rahmen der vorliegenden Arbeit wurde das Modellkorrekturverfahren, basierend auf der iterativen Anpassung eines F.E.-Modells an die identifizierten Eigenfrequenzen und Eigenformen des Spanngliedes, für die Bestimmung der Spanngliedkräfte verwendet. Dieses Verfahren ermöglicht die Berücksichtigung der Parameter (Schwingungslänge, Randbedingungen und effektive Biegesteifigkeit) bei der Identifikation der effektiven Spanngliedkräfte. Weiterhin ist eine Modellierung jeder beliebigen Spanngliedausbildung, z. B. bei unterschiedlichen Querschnitten in den Verankerungs- bzw. Umlenkbereichen, gewährleistet. Zur Anwendung bei der Ermittlung der Spanngliedkräfte wurde eine spezielle Methode, basierend auf den besonderen dynamischen Eigenschaften der Spannglieder, entwickelt, bei der die zuvor genannten Parameter innerhalb jedes Iterationsschrittes unabhängig korrigiert werden, was zur Robustheit des Identifikationsverfahrens beiträgt. Das entwickelte Verfahren ist in einem benutzerfreundlichen Programmsystem implementiert worden. Die erzielten Ergebnisse wurden mit dem allgemeinen Identifikationsprogramm UPDATE_g2 verglichen; dabei ist eine sehr gute Übereinstimmung festgestellt worden. Beim selbst entwickelten Verfahren wird die benötigte Rechenzeit auf ca. 30 % reduziert [100 sec à 30 sec]. Es bietet sich daher für die unmittelbare Auswertung vor Ort an. Die Parameteridentifikationsverfahren wurden an den Spanngliedern von insgesamt sechs Brücken (vier unterschiedliche Spannverfahren) angewendet. Die Anzahl der getesteten Spannglieder beträgt insgesamt 340. Die Abweichung zwischen den durch Schwingungs-messungen identifizierten und gemessenen (bei einer Brücke durch eine Abhebekontrolle) bzw. aufgebrachten Spanngliedkräften war kleiner als 3 %. Ferner wurden die Auswirkungen äußerer Einflüsse infolge Temperaturschwankungen und Verkehr bei den durchgeführten Messungen untersucht. Bei der praktischen Anwendung sind Besonderheiten aufgetreten, die durch die Verwendung des Modellkorrekturverfahrens weitgehend erfasst werden konnten. Zusammenfassend lässt sich sagen, dass die Verwendung dieses Verfahrens die Genauigkeit im Vergleich mit den bisherigen Schwingungsmessmethoden beachtlich erhöht. Ferner wird eine Erweiterung des Anwendungsbereiches auch auf Spezialfälle (z. B. bei einem unplanmäßigen Anliegen) gewährleistet.

Preconditioner updates applied to CFD model problems

In the present paper we concentrate on solving sequences of nonsymmetric linear systems with block structure arising from compressible flow problems. We attempt to improve the solution process by sharing part of the computational effort throughout the sequence. This is achieved by application of a cheap updating technique for preconditioners which we adapted in order to be used for our applications. Tested on three benchmark compressible flow problems, the strategy speeds up the entire computation with an acceleration being particularly pronounced in phases of instationary behavior.

Applied mathematical problem solving, modelling, applications, and links to other subjects

The paper will consist of three parts. In part I we shall present some background considerations which are necessary as a basis for what follows. We shall try to clarify some basic concepts and notions, and we shall collect the most important arguments (and related goals) in favour of problem solving, modelling and applications to other subjects in mathematics instruction. In the main part II we shall review the present state, recent trends, and prospective lines of development, both in empirical or theoretical research and in the practice of mathematics instruction and mathematics education, concerning (applied) problem solving, modelling, applications and relations to other subjects. In particular, we shall identify and discuss four major trends: a widened spectrum of arguments, an increased globality, an increased unification, and an extended use of computers. In the final part III we shall comment upon some important issues and problems related to our topic.

Information Exchange and Fusion in Dynamic and Heterogeneous Distributed Environments

Context awareness, dynamic reconfiguration at runtime and heterogeneity are key characteristics of future distributed systems, particularly in ubiquitous and mobile computing scenarios. The main contributions of this dissertation are theoretical as well as architectural concepts facilitating information exchange and fusion in heterogeneous and dynamic distributed environments. Our main focus is on bridging the heterogeneity issues and, at the same time, considering uncertain, imprecise and unreliable sensor information in information fusion and reasoning approaches. A domain ontology is used to establish a common vocabulary for the exchanged information. We thereby explicitly support different representations for the same kind of information and provide Inter-Representation Operations that convert between them. Special account is taken of the conversion of associated meta-data that express uncertainty and impreciseness. The Unscented Transformation, for example, is applied to propagate Gaussian normal distributions across highly non-linear Inter-Representation Operations. Uncertain sensor information is fused using the Dempster-Shafer Theory of Evidence as it allows explicit modelling of partial and complete ignorance. We also show how to incorporate the Dempster-Shafer Theory of Evidence into probabilistic reasoning schemes such as Hidden Markov Models in order to be able to consider the uncertainty of sensor information when deriving high-level information from low-level data. For all these concepts we provide architectural support as a guideline for developers of innovative information exchange and fusion infrastructures that are particularly targeted at heterogeneous dynamic environments. Two case studies serve as proof of concept. The first case study focuses on heterogeneous autonomous robots that have to spontaneously form a cooperative team in order to achieve a common goal. The second case study is concerned with an approach for user activity recognition which serves as baseline for a context-aware adaptive application. Both case studies demonstrate the viability and strengths of the proposed solution and emphasize that the Dempster-Shafer Theory of Evidence should be preferred to pure probability theory in applications involving non-linear Inter-Representation Operations.

First-principles electronic theory of non-collinear magnetic order in transition-metal nanowires

The structural, electronic and magnetic properties of one-dimensional 3d transition-metal (TM) monoatomic chains having linear, zigzag and ladder geometries are investigated in the frame-work of first-principles density-functional theory. The stability of long-range magnetic order along the nanowires is determined by computing the corresponding frozen-magnon dispersion relations as a function of the 'spin-wave' vector q. First, we show that the ground-state magnetic orders of V, Mn and Fe linear chains at the equilibrium interatomic distances are non-collinear (NC) spin-density waves (SDWs) with characteristic equilibrium wave vectors q that depend on the composition and interatomic distance. The electronic and magnetic properties of these novel spin-spiral structures are discussed from a local perspective by analyzing the spin-polarized electronic densities of states, the local magnetic moments and the spin-density distributions for representative values q. Second, we investigate the stability of NC spin arrangements in Fe zigzag chains and ladders. We find that the non-collinear SDWs are remarkably stable in the biatomic chains (square ladder), whereas ferromagnetic order (q =0) dominates in zigzag chains (triangular ladders). The different magnetic structures are interpreted in terms of the corresponding effective exchange interactions J(ij) between the local magnetic moments μ(i) and μ(j) at atoms i and j. The effective couplings are derived by fitting a classical Heisenberg model to the ab initio magnon dispersion relations. In addition they are analyzed in the framework of general magnetic phase diagrams having arbitrary first, second, and third nearest-neighbor (NN) interactions J(ij). The effect of external electric fields (EFs) on the stability of NC magnetic order has been quantified for representative monoatomic free-standing and deposited chains. We find that an external EF, which is applied perpendicular to the chains, favors non-collinear order in V chains, whereas it stabilizes the ferromagnetic (FM) order in Fe chains. Moreover, our calculations reveal a change in the magnetic order of V chains deposited on the Cu(110) surface in the presence of external EFs. In this case the NC spiral order, which was unstable in the absence of EF, becomes the most favorable one when perpendicular fields of the order of 0.1 V/Å are applied. As a final application of the theory we study the magnetic interactions within monoatomic TM chains deposited on graphene sheets. One observes that even weak chain substrate hybridizations can modify the magnetic order. Mn and Fe chains show incommensurable NC spin configurations. Remarkably, V chains show a transition from a spiral magnetic order in the freestanding geometry to FM order when they are deposited on a graphene sheet. Some TM-terminated zigzag graphene-nanoribbons, for example V and Fe terminated nanoribbons, also show NC spin configurations. Finally, the magnetic anisotropy energies (MAEs) of TM chains on graphene are investigated. It is shown that Co and Fe chains exhibit significant MAEs and orbital magnetic moments with in-plane easy magnetization axis. The remarkable changes in the magnetic properties of chains on graphene are correlated to charge transfers from the TMs to NN carbon atoms. Goals and limitations of this study and the resulting perspectives of future investigations are discussed.

A Theory of Quantitative Inference Applied to a Mechanical Design Compiler

This thesis presents the ideas underlying a computer program that takes as input a schematic of a mechanical or hydraulic power transmission system, plus specifications and a utility function, and returns catalog numbers from predefined catalogs for the optimal selection of components implementing the design. Unlike programs for designing single components or systems, the program provides the designer with a high level "language" in which to compose new designs. It then performs some of the detailed design process. The process of "compilation" is based on a formalization of quantitative inferences about hierarchically organized sets of artifacts and operating conditions. This allows the design compilation without the exhaustive enumeration of alternatives.

Asymptotically Zero Energy Computing Using Split-Level Charge Recovery Logic

The dynamic power requirement of CMOS circuits is rapidly becoming a major concern in the design of personal information systems and large computers. In this work we present a number of new CMOS logic families, Charge Recovery Logic (CRL) as well as the much improved Split-Level Charge Recovery Logic (SCRL), within which the transfer of charge between the nodes occurs quasistatically. Operating quasistatically, these logic families have an energy dissipation that drops linearly with operating frequency, i.e., their power consumption drops quadratically with operating frequency as opposed to the linear drop of conventional CMOS. The circuit techniques in these new families rely on constructing an explicitly reversible pipelined logic gate, where the information necessary to recover the energy used to compute a value is provided by computing its logical inverse. Information necessary to uncompute the inverse is available from the subsequent inverse logic stage. We demonstrate the low energy operation of SCRL by presenting the results from the testing of the first fully quasistatic 8 x 8 multiplier chip (SCRL-1) employing SCRL circuit techniques.