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The high cost of routing infrastructure makes checking theories about larger nets a very difficult and expensive task. One possible approach to fight this problem is the use of virtual instead of physical infrastructure. OPNet- IT Guru software is a suite designed to simulate large nets and present relevant information. This allows validating extensive changes before actually implementing them on a productive network or testing theories without the overhead of a physical infrastructure.
This minor thesis shows a way to optimise a generated oracle to achieve shorter runtimes. Shorter runtimes of test cases allows the execution of more test cases in the same time. The execution of more test cases leads to a higher confidence in the software-quality. Oracles can be derived from specifications. However specifications are used for different purposes and therefore are not necessarily executable. Even if the are executable it might be with only a high runtime. Those two facts come mostly from the use of quantifiers in the logic. If the quantifier-range is not bounded, respectively if the bounds are outside the target language-datatype-limits, the specification is too expressive to be exported into a program. Even if the bounds inside the used datatype-limits, the quantification is represented as a loop which leads to a runtime blowup, especially if quantifiers are nested. This work explains four different possibilities to reduce the execution time of the oracle by manipulating the quantified formular whereas this approach is only applicable if the quantified variables are of type Integer.
This thesis introduces fnnlib, a C++ library for recurrent neural network simulations that I developed between October 2009 and March 2010 at Osaka University's Graduate School of Engineering. After covering the theory behind recurrent neural networks, backpropagation through time, recurrent neural networks with parametric bias, continuous-time recurrent neural networks, and echo state networks, the design of the library is explained. All of the classes as well as their interrelationships are presented along with reasons as to why certain design decisions were made. Towards the end of the thesis, a small practical example is shown. Also, fnnlib is compared to other neural network libraries.
This paper documents the development of an abstract physics layer (APL) for Simspark. After short introductions to physics engines and Simspark, reasons why an APL was developed are explained. The biggest part of this paper describes the new design and why certain design choices were made based on requirements that arose during developement. It concludes by explaining how the new design was eventually implemented and what future possibilities the new design holds.