This thesis deals with the development of a tractor front loader scale which measures payload continuously, independent of the center of gravity of the payload, and unaffected of the position and movements of the loader. To achieve this, a mathematic model of a common front loader is simplified which makes it possible to identify its parameters by a repeatable and automatic procedure. By measuring accelerations as well as cylinder forces, the payload is determined continuously during the working process. Finally, a prototype was build and the scale was tested on a tractor.
In this thesis, collision-induced dissociation (CID) studies serve to elucidate relative stabilities and to determine bond strengths within a given structure type of transition metal complexes. The infrared multi photon dissociation (IRMPD) spectroscopy combined with density functional theory (DFT) allow for structural analysis and provide insights into the coordination sphere of transition metal centers. The used combination of CID and IRMPD experiments is a powerful tool to obtain a detailed and comprehensive characterization and understanding of interactions between transition metals and organic ligands. The compounds’ spectrum comprises mono- or oligonuclear transition metal complexes containing iron, palladium, and ruthenium as well as lanthanide containing single molecule magnets (SMM). The presented investigations on the different transition metal complexes reveal manifold effects for each species leading to valuable results. A fundamental understanding of metal to ligand interactions is mandatory for the development of new and better organometallic complexes with catalytic, optical or magnetic properties.
The main goal of this thesis is twofold. First, the thesis aims at bridging the gap between existing Pattern Recognition (PR) methods of automatic signature verification and the requirements for their application in forensic science. This gap, attributed by various factors ranging from system definition to evaluation, prevents automatic methods from being used by Forensic Handwriting Examiners (FHEs). Second, the thesis presents novel signature verification methods developed particularly considering the implications of forensic casework, and outperforming the state-of-the-art PR methods.
The first goal of the thesis is attributed by four important factors, i.e., data, terminology, output reporting, and how evaluation of automatic systems is carried out today. It is argued that traditionally the signature data used in PR are not actual/close representative of the real world data (especially that available in forensic cases). The systems trained on such data are, therefore, not suitable for forensic environments. This situation can be tackled by providing more realistic data to PR researchers. To this end, various signature and handwriting datasets are gathered in collaboration with FHEs and are made publicly available through the course of this thesis. A special attention is given to disguised signatures--where authentic authors purposefully make their signatures look like a forgery. This genre was at large neglected in PR research previously.
The terminology used, in the two communities - PR and FHEs, differ greatly. In fact, even in PR, there is no standard terminology and people often differ in the usage of various terms particularly related to various types of forged signatures/handwriting. The thesis presents a new terminology that is equally useful for both forensic scientists and PR researchers. The proposed terminology is hoped to increase the general acceptability of automatic signature analysis systems in forensic science.
The outputs reported by general signature verification systems are not acceptable for FHEs and courts as they are either binary (yes/no) or score (raw evidence) based on similarity/difference. The thesis describes that automatic systems should rather report the probability of observing the evidence (e.g., a certain similarity/difference score) given the signature belongs to the acclaimed identity, and the probability of observing the same evidence given the signature does not belong to the acclaimed identity. This will take automatic systems from hard decisions to soft decisions, thereby enabling them to report likelihood ratios that actually represent the evidential value of the score rather than the raw score (evidence).
When automatic systems report soft decisions (as in the form of likelihood ratios), the thesis argues that there must be some methods to evaluate such systems. This thesis presents one such adaptation. The thesis argues that the state-of-the-art evaluation methods, like equal error rate and area under curve, do not address the needs of forensic science. These needs require an assessment of the evidential value of signature verification, rather than a hard/pure classification (accept/reject binary decision). The thesis demonstrates and validates a relatively simple adaptation of the current verification methods based on the Bayesian inference dependent calibration of continuous scores rather than hard classifications (binary and/or score based classification).
The second goal of this thesis is to introduce various local features based techniques which are capable of performing signature verification in forensic cases and reporting results as anticipated by FHEs and courts. This is an important contribution of the thesis because of the following two reasons. First, to the best of author's knowledge, local feature descriptors are for the first time used for development of signature verification systems for forensic environments (particularly considering disguised signatures). Previously, such methods have been heavily used for recognition tasks, rather than verification of writing behaviors, such as character and digit recognition. Second, the proposed methods not only report the more traditional decisions (like scores-usually reported in PR) but also the Bayesian inference based likelihood ratios (suitable for courts and forensic cases).
Furthermore, the thesis also provides a detailed man vs. machine comparison for signature verification tasks. The men, in this comparison, are forensic scientists serving as forensic handwriting examiners and having experience of varying number of years. The machines are the local features based methods proposed in this thesis, along with various other state-of-the-art signature verification systems. The proposed methods clearly outperform the state-of-the-art systems, and sometimes the human experts.
Finally, the thesis details various tasks that have been performed in the areas closely related to signature verification and its application in forensic casework. These include, developing novel local feature based methods for extraction of signatures/handwritten text from document images, hyper-spectral image analysis for extraction of signatures from forensic documents, and analysis of on-line signatures acquired through specialized pens equipped with Accelerometer and Gyroscope. These tasks are important as they enable the thesis to take PR systems one step further close to direct application in forensic cases.
This thesis treats the application of configurational forces for the evaluation of fracture processes in Antarctic ice shelves. FE simulations are used to analyze the influence of geometric scales, material parameters and boundary conditions on single surface cracks. A break-up event at the Wilkins Ice Shelf that coincided with a major temperature drop motivates the consideration of frost wedging as a mechanism for ice shelf disintegration. An algorithm for the evaluation of the crack propagation direction is used to analyze the horizontal growth of rifts. Using equilibrium considerations for a viscoelastic fluid, a method is introduced to compute viscous volume forces from measured velocity fields as loads for a linear elastic fracture mechanical analysis.
We propose and study a strongly coupled PDE-ODE system with tissue-dependent degenerate diffusion and haptotaxis that can serve as a model prototype for cancer cell invasion through the
extracellular matrix. We prove the global existence of weak solutions and illustrate the model behaviour by numerical simulations for a two-dimensional setting.
We discuss the problem of evaluating a robust solution.
To this end, we first give a short primer on how to apply robustification approaches to uncertain optimization problems using the assignment problem and the knapsack problem as illustrative examples.
As it is not immediately clear in practice which such robustness approach is suitable for the problem at hand,
we present current approaches for evaluating and comparing robustness from the literature, and introduce the new concept of a scenario curve. Using the methods presented in this paper, an easy guide is given to the decision maker to find, solve and compare the best robust optimization method for his purposes.
Attention-awareness is a key topic for the upcoming generation of computer-human interaction. A human moves his or her eyes to visually attends to a particular region in a scene. Consequently, he or she can process visual information rapidly and efficiently without being overwhelmed by vast amount of information from the environment. Such a physiological function called visual attention provides a computer system with valuable information of the user to infer his or her activity and the surrounding environment. For example, a computer can infer whether the user is reading text or not by analyzing his or her eye movements. Furthermore, it can infer with which object he or she is interacting by recognizing the object the user is looking at. Recent developments of mobile eye tracking technologies enable us
to capture human visual attention in ubiquitous everyday environments. There are various types of applications where attention-aware systems may be effectively incorporated. Typical examples are augmented reality (AR) applications such as Wikitude which overlay virtual information onto physical objects. This type of AR application presents augmentative information of recognized objects to the user. However, if it presents information of all recognized objects at once, the over
ow of information could be obtrusive to the user. As a solution for such a problem, attention-awareness can be integrated into a system. If a
system knows to which object the user is attending, it can present only the information of
relevant objects to the user.
Towards attention-aware systems in everyday environments, this thesis presents approaches
for analysis of user attention to visual content. Using a state-of-the-art wearable eye tracking device, one can measure the user's eye movements in a mobile scenario. By capturing the user's eye gaze position in a scene and analyzing the image where the eyes focus, a computer can recognize the visual content the user is currently attending to. I propose several image analysis methods to recognize the user-attended visual content in a scene image. For example, I present an application called Museum Guide 2.0. In Museum Guide 2.0, image-based object recognition and eye gaze analysis are combined together to recognize user-attended objects in a museum scenario. Similarly, optical character recognition
(OCR), face recognition, and document image retrieval are also combined with eye gaze analysis to identify the user-attended visual content in respective scenarios. In addition to Museum Guide 2.0, I present other applications in which these combined frameworks are effectively used. The proposed applications show that the user can benefit from active information presentation which augments the attended content in a virtual environment with
a see-through head-mounted display (HMD).
In addition to the individual attention-aware applications mentioned above, this thesis
Furthermore, I present novel interaction methodologies for a see-through HMD using eye gaze input. A see-through HMD is a suitable device for a wearable attention-aware system for everyday environments because the user can also view his or her physical environment
through the display. I propose methods for the user's attention engagement estimation with the display, eye gaze-driven proactive user assistance functions, and a method for interacting
with a multi-focal see-through display.
Contributions of this thesis include:
• An overview of the state-of-the-art in attention-aware computer-human interaction
and attention-integrated image analysis.
• Methods for the analysis of user-attended visual content in various scenarios.
• Demonstration of the feasibilities and the benefits of the proposed user-attended visual content analysis methods with practical user-supportive applications.
• Methods for interaction with a see-through HMD using eye gaze.
• A comprehensive framework for recognition of user-attended visual content in a complex
scene where multiple visual information resources are present.
This thesis opens a novel field of wearable computer systems where computers can understand the user attention in everyday environments and provide with what the user wants. I will show the potential of such wearable attention-aware systems for everyday
environments for the next generation of pervasive computer-human interaction.
The central topic of this thesis is Alperin's weight conjecture, a problem concerning the representation theory of finite groups.
This conjecture, which was first proposed by J. L. Alperin in 1986, asserts that for any finite group the number of its irreducible Brauer characters coincides with the number of conjugacy classes of its weights. The blockwise version of Alperin's conjecture partitions this problem into a question concerning the number of irreducible Brauer characters and weights belonging to the blocks of finite groups.
A proof for this conjecture has not (yet) been found. However, the problem has been reduced to a question on non-abelian finite (quasi-) simple groups in the sense that there is a set of conditions, the so-called inductive blockwise Alperin weight condition, whose verification for all non-abelian finite simple groups implies the blockwise Alperin weight conjecture. Now the objective is to prove this condition for all non-abelian finite simple groups, all of which are known via the classification of finite simple groups.
In this thesis we establish the inductive blockwise Alperin weight condition for three infinite series of finite groups of Lie type: the special linear groups \(SL_3(q)\) in the case \(q>2\) and \(q \not\equiv 1 \bmod 3\), the Chevalley groups \(G_2(q)\) for \(q \geqslant 5\), and Steinberg's triality groups \(^3D_4(q)\).
In this thesis, we investigate several upcoming issues occurring in the context of conceiving and building a decision support system. We elaborate new algorithms for computing representative systems with special quality guarantees, provide concepts for supporting the decision makers after a representative system was computed, and consider a methodology of combining two optimization problems.
We review the original Box-Algorithm for two objectives by Hamacher et al. (2007) and discuss several extensions regarding coverage, uniformity, the enumeration of the whole nondominated set, and necessary modifications if the underlying scalarization problem cannot be solved to optimality. In a next step, the original Box-Algorithm is extended to the case of three objective functions to compute a representative system with desired coverage error. Besides the investigation of several theoretical properties, we prove the correctness of the algorithm, derive a bound on the number of iterations needed by the algorithm to meet the desired coverage error, and propose some ideas for possible extensions.
Furthermore, we investigate the problem of selecting a subset with desired cardinality from the computed representative system, the Hypervolume Subset Selection Problem (HSSP). We provide two new formulations for the bicriteria HSSP, a linear programming formulation and a \(k\)-link shortest path formulation. For the latter formulation, we propose an algorithm for which we obtain the currently best known complexity bound for solving the bicriteria HSSP. For the tricriteria HSSP, we propose an integer programming formulation with a corresponding branch-and-bound scheme.
Moreover, we address the issue of how to present the whole set of computed representative points to the decision makers. Based on common illustration methods, we elaborate an algorithm guiding the decision makers in choosing their preferred solution.
Finally, we step back and look from a meta-level on the issue of how to combine two given optimization problems and how the resulting combinations can be related to each other. We come up with several different combined formulations and give some ideas for the practical approach.
The Event Segmentation Theory (Kurby & Zacks, 2008; Zacks, Speer, Swallow, Braver, & Reynolds, 2007) explains the perceptual organization of an ongoing activity into meaningful events. The classical event segmentation task (Newtson, 1973) involves watching an online video and indicating with key presses the event boundaries, i.e., when one event ends and the next one begins. The resulting hierarchical organization of object-based coarse events and action-based fine events gives insight into various cognitive processes. I used the Event Segmentation Theory to develop assistance and training systems for assembly workers in industrial settings at various levels - experts, new hires, and intellectually disabled people. Therefore, the first scientific question I asked was whether online and offline event segmentation result in the same event boundaries. This is important because assembly work requires not only watching activities online but processing the information offline, e.g., while performing the assembly task. By developing a special software tool that enables assessment of offline event boundaries, I established that online perception and offline elaboration lead to similar event boundaries. This study supports prior work suggesting that instructions should be structured around event boundaries.
Secondly, I investigated the importance of fine versus coarse event boundaries when learning the sequence of steps in virtual training, both for novices and experts in car door assembly. I found memory, tested by ability to predict the next frame, to be enhanced for object-based coarse events from the nearest fine event boundary. However, virtual training did not improve memory for action-based fine events from the nearest coarse event boundary. I conjecture that trainees primarily acquire the sequence of object-based coarse events in an initial training. Based on differences found in memory performance between experts and novices, I conclude that memory for action-based fine events is dependent on expertise.
Thirdly, I used the Event Segmentation Theory to investigate whether the simple and repetitive assembly tasks offered at workshops for intellectually disabled persons utilize their full cognitive potential. I analyzed event segmentation performance of 32 intellectually disabled persons compared to 30 controls using a variety of event segmentation measures. I found specific deficits in event boundary detection and hierarchical organization of events for the intellectually disabled group. However, results suggest that hierarchical organization is task-dependent. Because the event segmentation task accounted for differences in general cognitive ability, I propose the event segmentation task as diagnostic method for the need for support in executing assembly tasks.
Based on these three studies, I argue that the Event Segmentation Theory offers a framework for assessment and assistance of important attentional, perceptual, and memory processes related to assembly tasks. I demonstrate how practical applications can make use of this framework for the development of new computer-based assistance and training systems that are tailored to the users’ need for support and improve their quality of life.