Fundamental studies of the application of wheat for malting and brewing

Wheat (Triticum aestivum L.) has a long tradition as a raw material for the production of malt and beer. While breeding and cultivation efforts for barley have been highly successful in creating agronomically and brew- technical optimal specialty cultivars that have become well established as brewing barley varieties, the picture is completely different for brewing wheat. An increasing wheat beer demand results in a rising amount of raw material. Wheat has been - and still is – grown almost exclusively for the baking industry. It is this high demand that defines most of the wheat breeding objectives; and these objectives are generally not favourable in brewing industry. It is of major interest to screen wheat varieties for brewing processability and to give more focus to wheat as a brewing cereal. To obtain fast and reliable predications about the suitability of wheat cultivars a new mathematical method was developed in this work. The method allows a selection based on generally accepted quality characteristics. As selection criteria the parameters raw protein, soluble nitrogen, Kolbach index, extract and viscosity were chosen. During a triannual cultivation series, wheat varieties were evaluated on their suitability for brewing as well as stability to environmental conditions. To gain a fundamental understanding of the complex malting process, microstructural changes were evaluated and visualized by confocal laser scanning and scanning electron microscopy. Furthermore, changes observed in the micrographs were verified and endorsed by metabolic changes using established malt attributes. The degradation and formation of proteins during malting is essential for the final beer quality. To visualise fundamental protein changes taking place during malting, samples of each single process step were analysed and fractioned according their solubility. Protein fractions were analysed using a Lab-on-a-chip technique as well as OFFgel analysis. In general, a different protein distribution of wheat compared to barley or oat could be confirmed. During the malting process a degradation of proteins to small peptides and amino acids could be observed in all four Osborn fractions. Furthermore, in this study a protein profiling was performed to evaluate changes during the mashing process as well as the influence of grist composition. Differences in specific protein peaks and profile were detected for all samples during mashing. This study investigated the suitability of wheat for malting and brewing industry and closed the scientifical gap of amylolytic, cytolytic and proteolytic changes during malting and mashing.

Average-case analysis of power consumption in embedded systems

Power efficiency is one of the most important constraints in the design of embedded systems since such systems are generally driven by batteries with limited energy budget or restricted power supply. In every embedded system, there are one or more processor cores to run the software and interact with the other hardware components of the system. The power consumption of the processor core(s) has an important impact on the total power dissipated in the system. Hence, the processor power optimization is crucial in satisfying the power consumption constraints, and developing low-power embedded systems. A key aspect of research in processor power optimization and management is “power estimation”. Having a fast and accurate method for processor power estimation at design time helps the designer to explore a large space of design possibilities, to make the optimal choices for developing a power efficient processor. Likewise, understanding the processor power dissipation behaviour of a specific software/application is the key for choosing appropriate algorithms in order to write power efficient software. Simulation-based methods for measuring the processor power achieve very high accuracy, but are available only late in the design process, and are often quite slow. Therefore, the need has arisen for faster, higher-level power prediction methods that allow the system designer to explore many alternatives for developing powerefficient hardware and software. The aim of this thesis is to present fast and high-level power models for the prediction of processor power consumption. Power predictability in this work is achieved in two ways: first, using a design method to develop power predictable circuits; second, analysing the power of the functions in the code which repeat during execution, then building the power model based on average number of repetitions. In the first case, a design method called Asynchronous Charge Sharing Logic (ACSL) is used to implement the Arithmetic Logic Unit (ALU) for the 8051 microcontroller. The ACSL circuits are power predictable due to the independency of their power consumption to the input data. Based on this property, a fast prediction method is presented to estimate the power of ALU by analysing the software program, and extracting the number of ALU-related instructions. This method achieves less than 1% error in power estimation and more than 100 times speedup in comparison to conventional simulation-based methods. In the second case, an average-case processor energy model is developed for the Insertion sort algorithm based on the number of comparisons that take place in the execution of the algorithm. The average number of comparisons is calculated using a high level methodology called MOdular Quantitative Analysis (MOQA). The parameters of the energy model are measured for the LEON3 processor core, but the model is general and can be used for any processor. The model has been validated through the power measurement experiments, and offers high accuracy and orders of magnitude speedup over the simulation-based method.