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2017


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Stationary and time-dependent heat transfer in paradigmatic many-body geometries

Asheichyk, Kiryl

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

icm

[BibTex]

2017


[BibTex]


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Non-equilibrium forces after temperature quenches in ideal fluids with conserved density

Hölzl, Christian

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

icm

[BibTex]

[BibTex]


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Numerical studies of active colloids at fluid interfaces

Peter, Toni

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

icm

[BibTex]

[BibTex]


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Collective dynamics of laterally confined active particles near fluid-fluid interfaces

Kistner, Irina

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

icm

[BibTex]

[BibTex]


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Self-diffusion of DNA grafted functional colloids in a crowded environment

Werner, M.

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

icm

[BibTex]

[BibTex]


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Electrostatic interaction between non-identical charged particles at an electrolyte interface

Schmetzer, Timo

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

icm

[BibTex]

[BibTex]


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Microscopic investigation of the Marangoni effect

Pöhnl, Matthias

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

icm

[BibTex]

[BibTex]


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Interfacial structure of a catalytic surface

Lipp, Melanie

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

icm

[BibTex]

[BibTex]

2015


Gaussian Process Optimization for Self-Tuning Control
Gaussian Process Optimization for Self-Tuning Control

Marco, A.

Polytechnic University of Catalonia (BarcelonaTech), October 2015 (mastersthesis)

am ics

PDF Project Page [BibTex]

2015


PDF Project Page [BibTex]


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Adaptive and Learning Concepts in Hydraulic Force Control

Doerr, A.

University of Stuttgart, September 2015 (mastersthesis)

am ics

[BibTex]

[BibTex]


Object Detection Using Deep Learning - Learning where to search using visual attention
Object Detection Using Deep Learning - Learning where to search using visual attention

Kloss, A.

Eberhard Karls Universität Tübingen, May 2015 (mastersthesis)

Abstract
Detecting and identifying the different objects in an image fast and reliably is an important skill for interacting with one’s environment. The main problem is that in theory, all parts of an image have to be searched for objects on many different scales to make sure that no object instance is missed. It however takes considerable time and effort to actually classify the content of a given image region and both time and computational capacities that an agent can spend on classification are limited. Humans use a process called visual attention to quickly decide which locations of an image need to be processed in detail and which can be ignored. This allows us to deal with the huge amount of visual information and to employ the capacities of our visual system efficiently. For computer vision, researchers have to deal with exactly the same problems, so learning from the behaviour of humans provides a promising way to improve existing algorithms. In the presented master’s thesis, a model is trained with eye tracking data recorded from 15 participants that were asked to search images for objects from three different categories. It uses a deep convolutional neural network to extract features from the input image that are then combined to form a saliency map. This map provides information about which image regions are interesting when searching for the given target object and can thus be used to reduce the parts of the image that have to be processed in detail. The method is based on a recent publication of Kümmerer et al., but in contrast to the original method that computes general, task independent saliency, the presented model is supposed to respond differently when searching for different target categories.

am

PDF Project Page [BibTex]


Robot Arm Tracking with Random Decision Forests
Robot Arm Tracking with Random Decision Forests

Widmaier, F.

Eberhard-Karls-Universität Tübingen, May 2015 (mastersthesis)

Abstract
For grasping and manipulation with robot arms, knowing the current pose of the arm is crucial for successful controlling its motion. Often, pose estimations can be acquired from encoders inside the arm, but they can have significant inaccuracy which makes the use of additional techniques necessary. In this master thesis, a novel approach of robot arm pose estimation is presented, that works on single depth images without the need of prior foreground segmentation or other preprocessing steps. A random regression forest is used, which is trained only on synthetically generated data. The approach improves former work by Bohg et al. by considerably reducing the computational effort both at training and test time. The forest in the new method directly estimates the desired joint angles while in the former approach, the forest casts 3D position votes for the joints, which then have to be clustered and fed into an iterative inverse kinematic process to finally get the joint angles. To improve the estimation accuracy, the standard training objective of the forest training is replaced by a specialized function that makes use of a model-dependent distance metric, called DISP. Experimental results show that the specialized objective indeed improves pose estimation and it is shown that the method, despite of being trained on synthetic data only, is able to provide reasonable estimations for real data at test time.

am

PDF Project Page [BibTex]

PDF Project Page [BibTex]


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Policy Search for Imitation Learning

Doerr, A.

University of Stuttgart, January 2015 (thesis)

am ics

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Fluctuational electrodynamics for nonlinear media

Soo, H.

Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

icm

[BibTex]

[BibTex]


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Evaporation and growth of liquid drops

Pöhnl, Ruben

Universität Stuttgart, Stuttgart, 2015 (mastersthesis)

icm

[BibTex]

[BibTex]

2008


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Entropic Forces on Bio-Molecules

Hansen-Goos, H.

Universität Stuttgart, Stuttgart, 2008 (phdthesis)

icm

link (url) [BibTex]

2008


link (url) [BibTex]


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Wetting of geometrically structured substrates

Marinescu, M.

Universität Stuttgart, Stuttgart, Germany, 2008 (mastersthesis)

icm

[BibTex]

[BibTex]


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Fluktuations- und Kapillarkräfte zwischen Kolloiden an fluiden Grenzflächen

Lehle, H.

Universität Stuttgart, Stuttgart, 2008 (phdthesis)

icm

link (url) [BibTex]

link (url) [BibTex]


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Microscopic calculation of line tensions

Merath, R.-J.

Universität Stuttgart, Stuttgart, 2008 (phdthesis)

icm

link (url) [BibTex]

link (url) [BibTex]


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Lattice model for fluid flow in narrow channels

Dotti, C.

Universität Stuttgart, Stuttgart, 2008 (phdthesis)

icm

[BibTex]

[BibTex]


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Critical Casimir forces

Mohry, T. F.

Universität Stuttgart, Stuttgart, 2008 (mastersthesis)

icm

[BibTex]

[BibTex]

2005


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Interplay between geometry and fluid properties

König, P.-M.

Universität Stuttgart, Stuttgart, 2005 (phdthesis)

icm

link (url) [BibTex]

2005


link (url) [BibTex]


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Molecular dynamics of wet granular media

Goll, C.

Universität Stuttgart, Stuttgart, 2005 (mastersthesis)

icm

[BibTex]

[BibTex]


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Grenzflächenfluktuationen binärer Flüssigkeiten

Hiester, T.

Universität Stuttgart, Stuttgart, 2005 (phdthesis)

icm

link (url) [BibTex]

link (url) [BibTex]