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In the practice of software engineering, project managers often face the problem of software project management.
It is related to resource constrained project scheduling
problem. In software project scheduling, main resources are considered to be the employees with some skill set and required amount of salary. The main purpose of software
project scheduling is to assign tasks of a project to the available employees such that the total cost and duration of the project are minimized, while keeping in check that
the constraints of software project scheduling are fulfilled. Software project scheduling (SPSP) has complex combined optimization issues and its search space increases exponentially when number of tasks and employees are increased, this makes software project scheduling problem (SPSP) a NP-Hard problem. The goal of software project scheduling problem is to minimize total cost and duration of project which makes it multi-objective problem. Many algorithms are proposed up till now that claim to give near optimal results for NP-Hard problems, but only few are there that gives feasible set of solutions for software project scheduling problem, but still we want to get more efficient algorithm to get feasible and efficient results.
Nowadays, most of the problems are being solved by using nature inspired algorithms because these algorithms provide the behavior of exploration and exploitation. For solving
software project scheduling (SPSP) some of these nature inspired algorithms have been used e.g. genetic algorithms, Ant Colony Optimization algorithm (ACO), Firefly etc.
Nature inspired algorithms like particle swarm optimization, genetic algorithms and Ant Colony Optimization algorithm provides more promising result than naive and greedy algorithms. However there is always a quest and room for more improvement. The main purpose of this research is to use bat algorithm to get efficient results and solutions for software project scheduling problem. In this work modified bat algorithm is implemented where a different approach of random walk is used. The contributions of this thesis are to: (1) To adapt and apply modified multi-objective bat algorithm for solving software project scheduling (SPSP) efficiently, (2) to adapt and apply other nature inspired algorithms like genetic algorithms for solving software project scheduling (SPSP) and (3) to compare and analyze the results obtained by applied nature inspired algorithms and provide the conclusion.
This work deals with the construction of a microscope for combined total internal reflection fluorescence (TIRF) and confocal microscopy. It is especially designed for single-molecule fluorescence spectroscopy. The design of the microscope body is based on the miCube (Hohlbein lab, Wageningen University, NL). The excitation and detection pathways were adapted to allow both TIRF and confocal illumination as well as camera and pointdetection for two color-channels to allow single-molecule Förster resonance transfer measurements
A number of real time PCR approaches have been published in the literature. In this thesis, the suitability of different real time PCR approaches using hydrolysis probes have been evaluated regarding PCR performance, cost effectiveness as well as handling. The effect of double-quenched probes as well as the impact of the increase of relative Flap endonuclease amount in quantitative real time PCR has been examined. In terms of genotyping a TaqMan™ assay, considered to be the gold-standard in this application, has been tested and compared to phosphorothioate modified probes, allele specific primers, SNAKE primers, an allele specific probe and primer assays as well as an assay using minor groove binder probes. Promising observations have been made in the case of double-quenched probes, phosphorothioate modified probes, SNAKE primers as well as minor groove binder probes.
The cultivation of mammalian cells in the third dimension has a great potential for a
wide application in regenerative medicine, pharmaceutical industry or cancer research.
An overview about actual 3-D cultivation techniques like hydrogels and porous scaffolds as well as their various materials and modifications is given in this thesis. Also different products and their implementation for a new application of 3-D cell
culture in a laboratory are described.
Die vorliegende Diplomarbeit befasst sich mit der Analyse, Auswertung und Empfehlung einer Berechnungsmethode für die axiale Klemmkraft und Wellenmuttern-Anzugsmoment bei Hochgenauigkeits-Axial-Schrägkugellagern für Gewindetriebe. Des Weiteren wird der Einfluss der Klemmkraft bzw. WellenmutternAnzugsmomentes auf unterschiedliche Lagersätze und –anordnungen überprüft und Korrekturfaktoren dafür erarbeitet
Decentralization is one of the key attributes associated with blockchain technology. Among the different developments in recent years, decentralized autonomous organizations (DAOs) have been of growing interest. DAOs are currently a key part of another emerging use case, namely decentralized science (DeSci). Given the novelty of the field, an integrative definition of DeSci has not been established, but some inherent concepts and ideas can be traced back to the Open Science movement. Although the DeSci movement has the potential to benefit the public, for example through funding underrepresented research areas or more inclusive and transparent research in general, some negative aspects of decentralization should not be neglected. Due to the novelty of blockchain and emerging use cases, research can and should precede mass adoption, to which this paper aims to contribute.
Massive multiple-input multiple-output (MIMO), eine Technik bei der die Basisstation einer Mobilfunkzelle mit einer großen Anzahl an Antennen ausgestattet ist, wird derzeit als eine vielversprechende Schlüsseltechnologie zur Erfüllung der Anforderungen zukünftiger drahtloser Kommunikationsnetze der fünften Generation betrachtet. Die zuversichtlichen Angaben über die Leistung solcher Systeme beruht allerdings auf einer theoretischen, bisher kaum praktisch verizierten Annahme, dass die drahtlosen Übertragungskanäle verschiedener Nutzer aufgrund der hohen Anzahl an Antennen voneinander unabhängig sind. Das heißt, dass sogenannte günstige Übertragungsbedingungen herrschen. Die vorliegende Masterarbeit untersucht diese neuartigen Systeme unter zwei verschiedenen Perspektiven.
Im ersten Teil dieser Arbeit wird der Einfluss von realistischen Übertragungsbedingungen auf die Performance von massive MIMO Systemen evaluiert. Dazu werden entsprechende numerische Systemsimulationen durchgeführt und mit den Ergebnissen von praktischen massive MIMO Messkampagnen verglichen.
Die Untersuchungen ergeben, dass die sogenannten günstigen Übertragungsbedingungen in realistischen Umgebungen nur bedingt beobachtet werden können. Daher führen traditionelle Kanalmodelle zu einer ungenauen Abschätzung der Leistung von praktischen massive MIMO Systemen. Um diesem Problem zu begegnen, wird deshalb eine neuartige Parametrisierung des traditionellen Kronecker-Modells vorgeschlagen, sodass relevante Kenngrößen realistischer Kanäle mit diesem Modell präzise widergespiegelt werden.
Anschließend folgt eine Untersuchung verschiedener Methoden zur Kanalschätzung in massive MIMO Systemen unter den verschiedenen Kanalmodellen mittels numerischer Simulationen. Die Experimente zeigen auf, dass Schätzmethoden, welche speziell für massive MIMO unter der Annahme von günstigen Übertragungsbedinungen hergeleitet wurden, eine signifikante Leistungsminderung unter realistischen Kanalmodellen erfahren.
Im zweiten Teil dieser Arbeit liegt der Fokus auf der Anwendung von massive MIMO Systemen in sogenannten Internet of Things (IoT) Netzwerken. Die typischerweise hohe Anzahl an aktiven IoT-Geräten macht die Anwendung von effizienten Scheduling-Algorithmen notwendig. Daher wird ein Downlink-Scheduling-Algorithmus präsentiert, welcher sich die Eigenschaften von massive MIMO Systemen und die typischen Anforderungen an die Datenraten von IoT-Geräten zunutze macht. Im Speziellen wird vorgeschlagen, die IoT-Nutzer in Gruppen aufzuteilen und die verschiedenen Gruppen nacheinander zu versorgen. Die Gruppengröße wird dabei mit Hilfe asymptotischer Eigenschaften von massive MIMO Systemen hergeleitet.
Um die Gruppenmitglieder zu selektieren, wird eine modifizierte Version des populären Semi-Orthogonal-User-Selection (SUS) Algorithmus vorgeschlagen. Die anschließend durchgeführten numerischen Simulationen bestätigen, dass die modifizierte Version von SUS die Nachteile des originalen Algorithmus eliminiert, was wiederum zu verbesserten Datenraten in dem betrachteten System führt.
Computationally solving eigenvalue problems is a central problem in numerical analysis and as such has been the subject of extensive study. In this thesis we present four different methods to compute eigenvalues, each with its own characteristics, strengths and weaknesses. After formally introducing the methods we use them in various numerical experiments to test speed of convergence, stability as well as performance when used to compute eigenfaces, denoise images and compute the eigenvector centrality measure of a graph.
The primary objective of this work and the research at the “Helmholtz-Zentrum für Umweltforschung” was to gain a deeper understanding of the basically transformation processes, especially for nitrogen species, in constructed wetlands. Therefore two different types of laboratory scale model systems, run with two different artificial wastewaters, had been observed for about 4 months. Data about the situation of three nitrogen species (ammonium, nitrate, nitrite), the physical condition of the pore water and the carbon sources contained by the water had been collected and compared. The present work will provide a summary about the actual knowledge of the microbial processes in constructed wetlands and the general character of such constructions. It will explain the different methods used to gain the data which will be later wards discussed with the aid of the created graphs in the final argumentation.
This Bachelor thesis investigates the learning rules of the Hebbian, Oja and BCM neuron models for their convergence to, and the stability of, the fixed points. Existing research is presented in a structured manner using consistent notation. Hebbian learning is neither convergent nor stable. Oja learning converges to a stable fixed point, which is the eigenvector corresponding to the largest eigenvalue of the covariance matrix of the input data. BCM learning converges to a fixed point which is stable, when assuming a discrete distribution of orthogonal inputs that occur with equal probability. Hebbian learning can therefore not be used in further applications, where convergence to a stable fixed point is required. Furthermore, this Bachelor thesis came to the conclusion that determining the fixed points of the BCM learning rule explicitly involves extensive calculation and other methods for verifying the stability of possible fixed points should be considered.