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Vorlesung
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5.04.4052 - Kohärente Optik
- Dr. Gerd Gülker, Dipl.-Phys.
Mittwoch: 10:00 - 12:00, wöchentlich (ab 19.10.2016), Ort: W33 0-003, W06 0-008
Termine am Mittwoch, 08.02.2017 10:00 - 12:00, Mittwoch, 10.05.2017 16:00 - 18:00, Ort: W33 0-003
Den Studierenden werden vertiefte Kenntnisse im Bereich der Optik mit dem Schwerpunkt der kohärenten Optik vermittelt. Sie werden mit aktuellen Forschungsergebnissen auf diesem Gebiet vertraut gemacht und erwerben dabei Fertigkeiten zum selbständigen Umgang mit entsprechender Fachliteratur. Sie erlangen Kompetenzen zur wissenschaftlichen Analyse komplexer physikalischer Sachverhalte sowie zur selbständigen Einordnung neuer Forschungsergebnisse einschließlich ihrer gesellschaftspolitischen Bedeutung.
Inhalte:
Wellenoptik, Wellenausbreitung, räumliche und zeitliche Kohärenz, Interferenz und Interferometrie, Beugung, Fourieroptik, optische Korrelation, astronomische Anwendungen, Speckle und Speckle-Messtechnik, Holografie, holografische Interferometrie, holografische Filterung, holografisch optische Elemente, digitale Holografie.
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5.04.4213 - Machine Learning I - Probabilistic Unsupervised Learning
Mittwoch: 10:00 - 12:00, wöchentlich (ab 19.10.2016), Vorlesung
The field of Machine Learning develops and provides methods for the analysis of data and signals. Typical application domains are computer hearing, computer vision, general pattern recognition and large-scale data analysis (recently often termed "Big Data"). Furthermore, Machine Learning methods serve as models for information processing and learning in humans and animals, and are often considered as part of artificial intelligence approaches.
This course gives an introduction to unsupervised learning methods, i.e., methods that extract knowledge from data without the requirement of explicit knowledge about individual data points. We will introduce a common probabilistic framework for learning and a methodology to derive learning algorithms for different types of tasks. Examples that are derived are algorithms for clustering, classification, component extraction, feature learning, blind source separation and dimensionality reduction. Relations to neural network models and learning in biological systems will be discussed were appropriate.
The course requires some programming skills, preferably in Matlab or Python. Further requirements are typical mathematical / analytical skills that are taught as part of Bachelor degrees in Physics, Mathematics, Statistics, Computer and Engineering Sciences. Course assignments will include analytical tasks and programming task which can be worked out in small groups.
The presented approach to unsupervised learning relies on Bayes' theorem and is therefore sometimes referred to as a Bayesian approach. It has many interesting relations to physics (e.g., statistical physics), statistics and mathematics (analysis, probability theory, stochastic) but the course's content will be developed independently of detailed prior knowledge in these fields.
Weblink: www.uni-oldenburg.de/ml
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5.06.602 - Biomass Energy I
- Dr. Alexandra Pehlken
- Prof. Dr. Michael Wark, Dipl.-Chem.
Mittwoch: 08:30 - 10:00, wöchentlich (ab 19.10.2016)
Termine am Donnerstag, 01.12.2016 08:30 - 10:00, Samstag, 21.01.2017 10:00 - 11:30
The students will understand the principles and potential uses for biomass as well as the shortcomings of biomass as a renewable energy. The students will develop an understanding of the growth and degradation of every type of biomass, as well as the basics of a balanced ecosystem and the sustainable use of biomass. Students gain basic understanding on biomass processing technologies. In cooperation with the Energy Systems & Society Module, one shall gain an understanding of the connection between man and the function of a healthy ecosystem and its preservation.
Competence:
The students gain competencies with critical discourse of competitive uses of biomass between human consumption, animal feed, raw material and fuel. The students are taught the issues concerning biomass transportation as well as the economic and ecological criteria involving its planning and use. They develop criteria, in order to address the complex relation between the future and a sustainable energy supply. The students gain competence to better the living conditions of rural inhabitants in developing countries through improved applications of biomass for daily energy needs.
Content:
Basic Understanding of:
• Nature or photosynthesis: chemical storage of solar energy; Efficiency of Plants
• Composition of biomass: sugar, starch, fat, oils, protein, lignin
• Knowledge of typical crop yield and energy content of various plants
• Typical energy crops in different climates
• Form and distribution of biomass uses in different geographic and climatic regions
• Traditional and modern energetic uses of biomass as well as the efficiency and technology
• Degradation process of biomass: Microorganisms, classification and metabolism (main degradation)
Sustainable Biomass Use
• Soil fertility, decrease and destruction of natural fertility
• Soil ecology
• Growth and diversity of biomass
• Roll of the microorganism in the metabolic cycle
Technology
The guiding theme are the principles of traditional and modern energetic use of biomass, the constraints and efficiencies for
food preparation, transport, and thermal and electrical energy production
• Biomass cookers, Improved Cook Stoves
• Wood gasification
• Biogas equipment
• Biodiesel production
• Ethanol production from sugarcane
• Methanol production
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