We intend to find optimal deterministic and randomized algorithms for three related problems: multivariate integration, parametric multivariate integration, and parametric initial value problems. The main interest is concentrated on the question, in how far randomization affects the precision of an approximation. We want to understand when and to which extent randomized algorithms are superior to deterministic ones.
All problems are studied for Banach space valued input functions. The analysis of Banach space valued problems is motivated by the investigation of scalar parametric problems; these can be understood as particular cases of Banach space valued problems. The gain achieved by randomization depends on the underlying Banach space.
For each problem, we introduce deterministic and randomized algorithms and provide the corresponding convergence analysis.
Moreover, we also provide lower bounds for the general Banach space valued settings, and thus, determine the complexity of the problems. It turns out that the obtained algorithms are order optimal in the deterministic setting. In the randomized setting, they are order optimal for certain classes of Banach spaces, which includes the L_p spaces and any finite dimensional Banach space. For general Banach spaces, they are optimal up to an arbitrarily small gap in the order of convergence.
This paper describes an audio interface written in VHDL, that connects the ADAU1761 audio codec on the Zedboard to the Zynq PL. Audio signals can be received in stereo from the line in jack and/or transmitted to the headphone out jack.
Nitrogen element is preponderant in Nature. Found in its simplest form as diatomic gas in the air, as well as in elaborated molecules such as the double helix of DNA, this element is indisputably essential for life. Indeed, nitrogen is omnipresent in all metabolic pathways.
With the advent of green chemistry, researchers attempt to functionalize arenes without pre-functionalization of the later for the establishment of C-C bond formation. Why not C-N bond formation?
We investigated new oxidative amination reactions by cross-dehydrogenative-coupling. Concerned by atom economy and green processes, our objectives were: 1) to obviate pre-activation or pre-oxidation of both C-H coupling partner and N-aminating agent. 2) to avoid the use of chelating directing group.
We achieved C-N bond formation for some classes of amines. Thus, we will describe the reactivity of cyclic secondary amines: carbazole, in presence of catalytic amount of ruthenium (II) and copper (II) to build the challenging C-N bond between two carbazoles. The initial mechanistic experiments will be present and discuss.
Then, we will describe more challenging hetero-coupling formation between diarylamines and carbazoles. The new ruthenium (II)/ copper (II) catalytic system allowed forming the ortho-N-carbazolation of diarylamines. This reaction performed under mild conditions (O2 as terminal oxidant) displays an unusual intramolecular N-H••N interaction in the novel class of compounds.
Finally, we will present a surprising metal free C-N bond formation between the ubiquitous phenols and the phenothiazines. Initially conducted in the presence of transition metals (RuII/CuII), this reaction proved to be efficient with the only effect of cumene and O2. Those components suggest a mechanism initiated by a Hock process. An initial infra-red analysis might point out a strong intramolecular O-H••N interaction in the resulting products.
These first elements of reactivity, developed within the laboratory for “modern dehydrogenative amination reactions”, will be presented and discussed.
In embedded systems, there is a trend of integrating several different functionalities on a common platform. This has been enabled by increasing processing power and the arise of integrated system-on-chips.
The composition of safety-critical and non-safety-critical applications results in mixed-criticality systems. Certification Authorities (CAs) demand the certification of safety-critical applications with strong confidence in the execution time bounds. As a consequence, CAs use conservative assumptions in the worst-case execution time (WCET) analysis which result in more pessimistic WCETs than the ones used by designers. The existence of certified safety-critical and non-safety-critical applications can be represented by dual-criticality systems, i.e., systems with two criticality levels.
In this thesis, we focus on the scheduling of mixed-criticality systems which are subject to certification. Scheduling policies cognizant of the mixed-criticality nature of the systems and the certification requirements are needed for efficient and effective scheduling. Furthermore, we aim at reducing the certification costs to allow faster modification and upgrading, and less error-prone certification. Besides certification aspects, requirements of different operational modes result in challenging problems for the scheduling process. Despite the mentioned problems, schedulers require a low runtime overhead for an efficient execution at runtime.
The presented solutions are centered around time-triggered systems which feature a low runtime overhead. We present a transformation to include event-triggered activities, represented by sporadic tasks, already into the offline scheduling process. Further, this transformation can also be applied on periodic tasks to shorten the length of schedule tables which reduces certification costs. These results can be used in our method to construct schedule tables which creates two schedule tables to fulfill the requirements of dual-criticality systems using mode changes at runtime. Finally, we present a scheduler based on the slot-shifting algorithm for mixed-criticality systems. In a first version, the method schedules dual-criticality jobs without the need for mode changes. An already certified schedule table can be used and at runtime, the scheduler reacts to the actual behavior of the jobs and thus, makes effective use of the available resources. Next, we extend this method to schedule mixed-criticality job sets with different operational modes. As a result, we can schedule jobs with varying parameters in different modes.
In this thesis, an approach is presented that turns the currently unstructured process of automotive hazard analysis and risk assessments (HRA), which relies on creativity techniques, into a structured, model-based approach that makes the HRA results less dependent on experts' experience, more consistent, and gives them higher quality. The challenge can be subdivided into two steps. The first step is to improve the HRA as it is performed in current practice. The second step is to go beyond the current practice and consider not only single service failures as relevant hazards, but also multiple service failures. For the first step, the most important aspect is to formalize the operational situation of the system and to determine its likelihood. Current approaches use natural-language textual descriptions, which makes it hard to ensure consistency and increase efficiency through reuse. Furthermore, due to ambiguity in natural language, it is difficult to ensure consistent likelihood estimates for situations.
The main aspect of the second step is that considering multiple service failures as hazards implies that one needs to analyze an exponential number of hazards. Due to the fact that hazard assessments are currently done purely manually, considering multiple service failures is not possible. The only way to approach this challenge is to formalize the HRA and make extensive use of automation support.
In SAHARA we handle these challenges by first introducing a model-based representation of an HRA with GOBI. Based on this, we formalized the representation of operational situations and their likelihood assessment in OASIS and HEAT, respectively. We show that more consistent situation assessments are possible and that situations (including their likelihood) can be efficiently reused. The second aspect, coping with multiple service failures, is addressed in ARID. We show that using our tool-supported HRA approach, 100% coverage of all possible hazards (including multiple service failures) can be achieved by relying on very limited manual effort. We furthermore show that not considering multiple service failures results in insufficient safety goals.
Today's ubiquity of visual content as driven by the availability of broadband Internet, low-priced storage, and the omnipresence of camera equipped mobile devices conveys much of our thinking and feeling as individuals and as a society. As a result the growth of video repositories is increasing at enourmous rates with content now being embedded and shared through social media. To make use of this new form of social multimedia, concept detection, the automatic mapping of semantic concepts and video content has to be extended such that concept vocabularies are synchronized with current real-world events, systems can perform scalable concept learning with thousands of concepts, and high-level information such as sentiment can be extracted from visual content. To catch up with these demands the following three contributions are made in this thesis: (i) concept detection is linked to trending topics, (ii) visual learning from web videos is presented including the proper treatment of tags as concept labels, and (iii) the extension of concept detection with adjective noun pairs for sentiment analysis is proposed.
In order for concept detection to satisfy users' current information needs, the notion of fixed concept vocabularies has to be reconsidered. This thesis presents a novel concept learning approach built upon dynamic vocabularies, which are automatically augmented with trending topics mined from social media. Once discovered, trending topics are evaluated by forecasting their future progression to predict high impact topics, which are then either mapped to an available static concept vocabulary or trained as individual concept detectors on demand. It is demonstrated in experiments on YouTube video clips that by a visual learning of trending topics, improvements of over 100% in concept detection accuracy can be achieved over static vocabularies (n=78,000).
To remove manual efforts related to training data retrieval from YouTube and noise caused by tags being coarse, subjective and context-depedent, this thesis suggests an automatic concept-to-query mapping for the retrieval of relevant training video material, and active relevance filtering to generate reliable annotations from web video tags. Here, the relevance of web tags is modeled as a latent variable, which is combined with an active learning label refinement. In experiments on YouTube, active relevance filtering is found to outperform both automatic filtering and active learning approaches, leading to a reduction of required label inspections by 75% as compared to an expert annotated training dataset (n=100,000).
Finally, it is demonstrated, that concept detection can serve as a key component to infer the sentiment reflected in visual content. To extend concept detection for sentiment analysis, adjective noun pairs (ANP) as novel entities for concept learning are proposed in this thesis. First a large-scale visual sentiment ontology consisting of 3,000 ANPs is automatically constructed by mining the web. From this ontology a mid-level representation of visual content – SentiBank – is trained to encode the visual presence of 1,200 ANPs. This novel approach of visual learning is validated in three independent experiments on sentiment prediction (n=2,000), emotion detection (n=807) and pornographic filtering (n=40,000). SentiBank is shown to outperform known low-level feature representations (sentiment prediction, pornography detection) or perform comparable to state-of-the art methods (emotion detection).
Altogether, these contributions extend state-of-the-art concept detection approaches such that concept learning can be done autonomously from web videos on a large-scale, and can cope with novel semantic structures such as trending topics or adjective noun pairs, adding a new dimension to the understanding of video content.
The goal of this work is to develop statistical natural language models and processing techniques
based on Recurrent Neural Networks (RNN), especially the recently introduced Long Short-
Term Memory (LSTM). Due to their adapting and predicting abilities, these methods are more
robust, and easier to train than traditional methods, i.e., words list and rule-based models. They
improve the output of recognition systems and make them more accessible to users for browsing
and reading. These techniques are required, especially for historical books which might take
years of effort and huge costs to manually transcribe them.
The contributions of this thesis are several new methods which have high-performance computing and accuracy. First, an error model for improving recognition results is designed. As
a second contribution, a hyphenation model for difficult transcription for alignment purposes
is suggested. Third, a dehyphenation model is used to classify the hyphens in noisy transcription. The fourth contribution is using LSTM networks for normalizing historical orthography.
A size normalization alignment is implemented to equal the size of strings, before the training
phase. Using the LSTM networks as a language model to improve the recognition results is
the fifth contribution. Finally, the sixth contribution is a combination of Weighted Finite-State
Transducers (WFSTs), and LSTM applied on multiple recognition systems. These contributions
will be elaborated in more detail.
Context-dependent confusion rules is a new technique to build an error model for Optical
Character Recognition (OCR) corrections. The rules are extracted from the OCR confusions
which appear in the recognition outputs and are translated into edit operations, e.g., insertions,
deletions, and substitutions using the Levenshtein edit distance algorithm. The edit operations
are extracted in a form of rules with respect to the context of the incorrect string to build an
error model using WFSTs. The context-dependent rules assist the language model to find the
best candidate corrections. They avoid the calculations that occur in searching the language
model and they also make the language model able to correct incorrect words by using context-
dependent confusion rules. The context-dependent error model is applied on the university of
Washington (UWIII) dataset and the Nastaleeq script in Urdu dataset. It improves the OCR
results from an error rate of 1.14% to an error rate of 0.68%. It performs better than the
state-of-the-art single rule-based which returns an error rate of 1.0%.
This thesis describes a new, simple, fast, and accurate system for generating correspondences
between real scanned historical books and their transcriptions. The alignment has many challenges, first, the transcriptions might have different modifications, and layout variations than the
original book. Second, the recognition of the historical books have misrecognition, and segmentation errors, which make the alignment more difficult especially the line breaks, and pages will
not have the same correspondences. Adapted WFSTs are designed to represent the transcription. The WFSTs process Fraktur ligatures and adapt the transcription with a hyphenations
model that allows the alignment with respect to the varieties of the hyphenated words in the line
breaks of the OCR documents. In this work, several approaches are implemented to be used for
the alignment such as: text-segments, page-wise, and book-wise approaches. The approaches
are evaluated on German calligraphic (Fraktur) script historical documents dataset from “Wan-
derungen durch die Mark Brandenburg” volumes (1862-1889). The text-segmentation approach
returns an error rate of 2.33% without using a hyphenation model and an error rate of 2.0%
using a hyphenation model. Dehyphenation methods are presented to remove the hyphen from
the transcription. They provide the transcription in a readable and reflowable format to be used
for alignment purposes. We consider the task as classification problem and classify the hyphens
from the given patterns as hyphens for line breaks, combined words, or noise. The methods are
applied on clean and noisy transcription for different languages. The Decision Trees classifier
returns better performance on UWIII dataset and returns an accuracy of 98%. It returns 97%
on Fraktur script.
A new method for normalizing historical OCRed text using LSTM is implemented for different texts, ranging from Early New High German 14th - 16th centuries to modern forms in New
High German applied on the Luther bible. It performed better than the rule-based word-list
approaches. It provides a transcription for various purposes such as part-of-speech tagging and
n-grams. Also two new techniques are presented for aligning the OCR results and normalize the
size by using adding Character-Epsilons or Appending-Epsilons. They allow deletion and insertion in the appropriate position in the string. In normalizing historical wordforms to modern
wordforms, the accuracy of LSTM on seen data is around 94%, while the state-of-the-art combined rule-based method returns 93%. On unseen data, LSTM returns 88% and the combined
rule-based method returns 76%. In normalizing modern wordforms to historical wordforms, the
LSTM delivers the best performance and returns 93.4% on seen data and 89.17% on unknown
In this thesis, a deep investigation has been done on constructing high-performance language
modeling for improving the recognition systems. A new method to construct a language model
using LSTM is designed to correct OCR results. The method is applied on UWIII and Urdu
script. The LSTM approach outperforms the state-of-the-art, especially for unseen tokens
during training. On the UWIII dataset, the LSTM returns reduction in OCR error rates from
1.14% to 0.48%. On the Nastaleeq script in Urdu dataset, the LSTM reduces the error rate
from 6.9% to 1.58%.
Finally, the integration of multiple recognition outputs can give higher performance than a
single recognition system. Therefore, a new method for combining the results of OCR systems is
explored using WFSTs and LSTM. It uses multiple OCR outputs and votes for the best output
to improve the OCR results. It performs better than the ISRI tool, Pairwise of Multiple Sequence and it helps to improve the OCR results. The purpose is to provide correct transcription
so that it can be used for digitizing books, linguistics purposes, N-grams, and part-of-speech
tagging. The method consists of two alignment steps. First, two recognition systems are aligned
using WFSTs. The transducers are designed to be more flexible and compatible with the different symbols in line and page breaks to avoid the segmentation and misrecognition errors.
The LSTM model then is used to vote the best candidate correction of the two systems and
improve the incorrect tokens which are produced during the first alignment. The approaches
are evaluated on OCRs output from the English UWIII and historical German Fraktur dataset
which are obtained from state-of-the-art OCR systems. The Experiments show that the error
rate of ISRI-Voting is 1.45%, the error rate of the Pairwise of Multiple Sequence is 1.32%, the
error rate of the Line-to-Page alignment is 1.26% and the error rate of the LSTM approach has
the best performance with 0.40%.
The purpose of this thesis is to contribute methods providing correct transcriptions corresponding to the original book. This is considered to be the first step towards an accurate and
more effective use of the documents in digital libraries.
The heterogeneity of today's access possibilities to wireless networks imposes challenges for efficient mobility support and resource management across different Radio Access Technologies (RATs). The current situation is characterized by the coexistence of various wireless communication systems, such as GSM, HSPA, LTE, WiMAX, and WLAN. These RATs greatly differ with respect to coverage, spectrum, data rates, Quality of Service (QoS), and mobility support.
In real systems, mobility-related events, such as Handover (HO) procedures, directly affect resource efficiency and End-To-End (E2E) performance, in particular with respect to signaling efforts and users' QoS. In order to lay a basis for realistic multi-radio network evaluation, a novel evaluation methodology is introduced in this thesis.
A central hypothesis of this thesis is that the consideration and exploitation of additional information characterizing user, network, and environment context, is beneficial for enhancing Heterogeneous Access Management (HAM) and Self-Optimizing Networks (SONs). Further, Mobile Network Operator (MNO) revenues are maximized by tightly integrating bandwidth adaptation and admission control mechanisms as well as simultaneously accounting for user profiles and service characteristics. In addition, mobility robustness is optimized by enabling network nodes to tune HO parameters according to locally observed conditions.
For establishing all these facets of context awareness, various schemes and algorithms are developed and evaluated in this thesis. System-level simulation results demonstrate the potential of context information exploitation for enhancing resource utilization, mobility support, self-tuning network operations, and users' E2E performance.
In essence, the conducted research activities and presented results motivate and substantiate the consideration of context awareness as key enabler for cognitive and autonomous network management. Further, the performed investigations and aspects evaluated in the scope of this thesis are highly relevant for future 5G wireless systems and current discussions in the 5G infrastructure Public Private Partnership (PPP).
This work establishes the novel category of coordinated Wireless Backhaul Networks (WBNs) for energy-autarkic point-to-point radio backhauling. The networking concept is based on three major building blocks: cost-efficient radio transceiver hardware, a self-organizing network operations framework, and power supply from renewable energy sources. The aim of this novel backhauling approach is to combine carrier-grade network performance with reduced maintenance effort as well as independent and self-sufficient power supply. In order to facilitate the success prospects of this concept, the thesis comprises the following major contributions: Formal, multi-domain system model and evaluation methodology
First, adapted from the theory of cyber-physical systems, the author devises a multi-domain evaluation methodology and a system-level simulation framework for energy-autarkic coordinated WBNs, including a novel balanced scorecard concept. Second, the thesis specifically addresses the topic of Topology Control (TC) in point-to-point radio networks and how it can be exploited for network management purposes. Given a set of network nodes equipped with multiple radio transceivers and known locations, TC continuously optimizes the setup and configuration of radio links between network nodes, thus supporting initial network deployment, network operation, as well as topology re-configuration. In particular, the author shows that TC in WBNs belongs to the class of NP-hard quadratic assignment problems and that it has significant impact in operational practice, e.g., on routing efficiency, network redundancy levels, service reliability, and energy consumption. Two novel algorithms focusing on maximizing edge connectivity of network graphs are developed.
Finally, this work carries out an analytical benchmarking and a numerical performance analysis of the introduced concepts and algorithms. The author analytically derives minimum performance levels of the the developed TC algorithms. For the analyzed scenarios of remote Alpine communities and rural Tanzania, the evaluation shows that the algorithms improve energy efficiency and more evenly balance energy consumption across backhaul nodes, thus significantly increasing the number of available backhaul nodes compared to state-of-the-art TC algorithms.
The work consists of two parts.
In the first part an optimization problem of structures of linear elastic material with contact modeled by Robin-type boundary conditions is considered. The structures model textile-like materials and possess certain quasiperiodicity properties. The homogenization method is used to represent the structures by homogeneous elastic bodies and is essential for formulations of the effective stress and Poisson's ratio optimization problems. At the micro-level, the classical one-dimensional Euler-Bernoulli beam model extended with jump conditions at contact interfaces is used. The stress optimization problem is of a PDE-constrained optimization type, and the adjoint approach is exploited. Several numerical results are provided.
In the second part a non-linear model for simulation of textiles is proposed. The yarns are modeled by hyperelastic law and have no bending stiffness. The friction is modeled by the Capstan equation. The model is formulated as a problem with the rate-independent dissipation, and the basic continuity and convexity properties are investigated. The part ends with numerical experiments and a comparison of the results to a real measurement.