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This work presents a new concept to implement the elliptic curve point multiplication (PM). This computation is based on a new modular arithmetic over Gaussian integer fields. Gaussian integers are a subset of the complex numbers such that the real and imaginary parts are integers. Since Gaussian integer fields are isomorphic to prime fields, this arithmetic is suitable for many elliptic curves. Representing the key by a Gaussian integer expansion is beneficial to reduce the computational complexity and the memory requirements of secure hardware implementations, which are robust against attacks. Furthermore, an area-efficient coprocessor design is proposed with an arithmetic unit that enables Montgomery modular arithmetic over Gaussian integers. The proposed architecture and the new arithmetic provide high flexibility, i.e., binary and non-binary key expansions as well as protected and unprotected PM calculations are supported. The proposed coprocessor is a competitive solution for a compact ECC processor suitable for applications in small embedded systems.
This thesis deals with the object tracking problem of multiple extended objects. For instance, this tracking problem occurs when a car with sensors drives on the road and detects multiple other cars in front of it. When the setup between the senor and the other cars is in a such way that multiple measurements are created by each single car, the cars are called extended objects. This can occur in real world scenarios, mainly with the use of high resolution sensors in near field applications. Such a near field scenario leads a single object to occupy several resolution cells of the sensor so that multiple measurements are generated per scan. The measurements are additionally superimposed by the sensor’s noise. Beside the object generated measurements, there occur false alarms, which are not caused by any object and sometimes in a sensor scan, single objects could be missed so that they not generate any measurements.
To handle these scenarios, object tracking filters are needed to process the sensor measurements in order to obtain a stable and accurate estimate of the objects in each sensor scan. In this thesis, the scope is to implement such a tracking filter that handles the extended objects, i.e. the filter estimates their positions and extents. In context of this, the topic of measurement partitioning occurs, which is a pre-processing of the measurement data. With the use of partitioning, the measurements that are likely generated by one object are put into one cluster, also called cell. Then, the obtained cells are processed by the tracking filter for the estimation process. The partitioning of measurement data is a crucial part for the performance of tracking filter because insufficient partitioning leads to bad tracking performance, i.e. inaccurate object estimates.
In this thesis, a Gaussian inverse Wishart Probability Hypothesis Density (GIW-PHD) filter was implemented to handle the multiple extended object tracking problem. Within this filter framework, the number of objects are modelled as Random Finite Sets (RFSs) and the objects’ extent as random matrices (RM). The partitioning methods that are used to cluster the measurement data are existing ones as well as a new approach that is based on likelihood sampling methods. The applied classical heuristic methods are Distance Partitioning (DP) and Sub-Partitioning (SP), whereas the proposed likelihood-based approach is called Stochastic Partitioning (StP). The latter was developed in this thesis based on the Stochastic Optimisation approach by Granström et al. An implementation, including the StP method and its integration into the filter framework, is provided within this thesis.
The implementations, using the different partitioning methods, were tested on simulated random multi-object scenarios and in a fixed parallel tracking scenario using Monte Carlo methods. Further, a runtime analysis was done to provide an insight into the computational effort using the different partitioning methods. It emphasized, that the StP method outperforms the classical partitioning methods in scenarios, where the objects move spatially close. The filter using StP performs more stable and with more accurate estimates. However, this advantage is associated with a higher computational effort compared to the classical heuristic partitioning methods.
The McEliece cryptosystem is a promising candidate for post-quantum public-key encryption. In this work, we propose q-ary codes over Gaussian integers for the McEliece system and a new channel model. With this one Mannheim error channel, errors are limited to weight one. We investigate the channel capacity of this channel and discuss its relation to the McEliece system. The proposed codes are based on a simple product code construction and have a low complexity decoding algorithm. For the one Mannheim error channel, these codes achieve a higher error correction capability than maximum distance separable codes with bounded minimum distance decoding. This improves the work factor regarding decoding attacks based on information-set decoding.
If the process contains a delay (dead time), the Nyquist criterion is well suited to derive a PI or PID tuning rule because the delay is taken into account without approximation. The tuning of the speed of the closed loop enters naturally by the crossover frequency. The goal of robustness and performance is translated into the phase margin.
Using multi-camera matching techniques for 3d reconstruction there is usually the trade-off between the quality of the computed depth map and the speed of the computations. Whereas high quality matching methods take several seconds to several minutes to compute a depth map for one set of images, real-time methods achieve only low quality results. In this paper we present a multi-camera matching method that runs in real-time and yields high resolution depth maps. Our method is based on a novel multi-level combination of normalized cross correlation, deformed matching windows based on the multi-level depth map information, and sub-pixel precise disparity maps. The whole process is implemented completely on the GPU. With this approach we can process four 0.7 megapixel images in 129 milliseconds to a full resolution 3d depth map. Our technique is tailored for the recognition of non-technical shapes, because our target application is face recognition.
This research project has been awarded as part of the research competition organized by Connect2Recover, which is a global initiative by the International Telecommunication Union (ITU) with the priority of reinforcing and strengthening the digital infrastructure and ecosystems of developing countries. Carried out by an international and transdisciplinary research consortium, the project sets out to analyze the prospects of digital federation and data sharing within the context of Botswana. Considering the country’s stage of economic and digital development, the project team identified Botswana’s smallholder agricultural sector as the most important area of digital transformation given the development need of the country’s primary sector.
Derived from semi-structured interviews, a focus group, as well as secondary research, the project team developed a digital transformation roadmap based on three development stages: (a) crowdfarming pilot, (b) crowdfarming marketplace, and (c) digital ecosystem for smallholder agriculture. Based on a detailed review of Botswana’s smallholder agriculture and the government’s digitalization strategy, the report envisions each phase, especially the pilot project, in terms of a minimal viable product. This is to consider the low level of digital penetration of Botswana’s primary sector, while providing an incentive to connect smallholders with consumers, traders, and retailers.
The project team has been successful in receiving commitment from actual smallholder farmers, the farmer association and government, as well as support for the idea of developing a crowdfarming marketplace as a novel production model and, eventually, a digital agriculture ecosystem for smallholder farmers, livestock producers, and agricultural technology companies and start-ups. The report is a proposal for a phase-one pilot project with the objective to advance smallholder agribusiness in Botswana.
This paper analyses international cooperation in alternative energy production research and development. Therefore, patents of the technological domain, registered at the European Patent Office from 1997 until 2016, are analysed. International cooperation is considered when patents involve co-assignment or co-inventorship comprising two or more different countries. Generally, international R&D cooperation tends to be increasing over time in alternative energy production. In total, 2234 co-patents from 87 countries are identified. Through social network analysis the cooperative relationships between countries are examined. The most significant states of the network are the United States of America and Germany. Innovative clusters and strong partnerships are identified. Alternative energy technologies that involve international cooperation most extensively are harnessing energy from manmade waste, solar energy and bio-fuels. The paper clarifies which countries are cooperating with each other for what purpose. The findings can be used for establishing R&D strategies in the domain of alternative energy production.
While driving, stress is caused by situations in which the driver estimates their ability to manage the driving demands as insufficient or loses the capability to handle the situation. This leads to increased numbers of driver mistakes and traffic violations. Additional stressing factors are time pressure, road conditions, or dislike for driving. Therefore, stress affects driver and road safety. Stress is classified into two categories depending on its duration and the effects on the body and psyche: short-term eustress and constantly present distress, which causes degenerative effects. In this work, we focus on distress. Wearable sensors are handy tools for collecting biosignals like heart rate, activity, etc. Easy installation and non-intrusive nature make them convenient for calculating stress. This study focuses on the investigation of stress and its implications. Specifically, the research conducts an analysis of stress within a select group of individuals from both Spain and Germany. The primary objective is to examine the influence of recognized psychological factors, including personality traits such as neuroticism, extroversion, psychoticism, stress and road safety. The estimation of stress levels was accomplished through the collection of physiological parameters (R-R intervals) using a Polar H10 chest strap. We observed that personality traits, such as extroversion, exhibited similar trends during relaxation, with an average heart rate 6% higher in Spain and 3% higher in Germany. However, while driving, introverts, on average, experienced more stress, with rates 4% and 1% lower than extroverts in Spain and Germany, respectively.
The Universal Serial Bus (USB) is a worldwide standard for communication between peripherals. Nowadays USB interfaces are integrated in almost every device. It will be used to connect peripherals and computers. USB devices communicate between pieces of hardware, i.e., cable, plug and socket. Thus, there exists different standardized communication protocols depending on the application. In case of different communication protocols, it is necessary to verify them, that devices, no matter of country, can communicate to each other.
The verifying process is very important in order that companies can sell products with such interfaces and their designated logo, to guaranty a certain standard, which is provided all over the world. Devices have to complete various test procedures to get certified. Otherwise a company is not allowed to use logos ore designations, i.e., USB or information about data rates, i.e., SuperSpeed. Furthermore, successfully completed test procedures prove that a device works properly based on a professional method.
The Human-Machine-Interface (HMI) device family from the company Marquardt Verwaltungs GmbH, is using the USB interface for service and data exchange purposes. The service application is realized through a Virtual COM Port (VCP), based on the Communication Device Class (CDC) of USB. On the other side they want to use the Media Transfer Protocol (MTP) based on the Still Image Capture Device class for data exchange between the HMI device and a computer. Of course, the integrated circuit, which implements the USB interface on the circuit board of the HMI device has to be verified, too. The verification will be performed through an external company. In contrast, the communication protocols do not need a verification but must be examined. The identification of an USB class in an operating system does neither guaranty a proper functionality nor comply with a professional scientific method.
To accelerate the development of a project as well as to reduce the production costs, it is a significant advantage to own a test environment. Microsoft provides the possibility to verify devices on Windows operating systems. Therefor they invented the Windows Certification Program, which contains software that can be used for verification purposes. One of them is the Windows Hardware Certification Kit (HCK) we want to set up and set the HMI device under test, to examine the implementation of MTP.
Thus, it is possible to use the HCK test setup during a development process to examine a current implementation without a big effort, i.e., cooperation with an external company or similarly approaches, which delays the whole development process by far.
Specific climate adaptation and resilience measures can be efficiently designed and implemented at regional and local levels. Climate and environmental databases are critical for achieving the sustainable development goals (SDGs) and for efficiently planning and implementing appropriate adaptation measures. Available federated and distributed databases can serve as necessary starting points for municipalities to identify needs, prioritize resources, and allocate investments, taking into account often tight budget constraints. High-quality geospatial, climate, and environmental data are now broadly available and remote sensing data, e.g., Copernicus services, will be critical. There are forward-looking approaches to use these datasets to derive forecasts for optimizing urban planning processes for local governments. On the municipal level, however, the existing data have only been used to a limited extent. There are no adequate tools for urban planning with which remote sensing data can be merged and meaningfully combined with local data and further processed and applied in municipal planning and decision-making. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services, and procedures, such as the development of practical technical tools that capture different remote sensing and in-situ data sets for validation and further processing. CoKLIMAx will be used to develop a scalable toolbox for urban planning to increase climate resilience. Focus areas of the project will be water (e.g., soil sealing, stormwater drainage, retention, and flood protection), urban (micro)climate (e.g., heat islands and air flows), and vegetation (e.g., greening strategy, vegetation monitoring/vitality). To this end, new digital process structures will be embedded in local government to enable better policy decisions for the future.
In order to ensure sufficient recovery of the human body and brain, healthy sleep is indispensable. For this purpose, appropriate therapy should be initiated at an early stage in the case of sleep disorders. For some sleep disorders (e.g., insomnia), a sleep diary is essential for diagnosis and therapy monitoring. However, subjective measurement with a sleep diary has several disadvantages, requiring regular action from the user and leading to decreased comfort and potential data loss. To automate sleep monitoring and increase user comfort, one could consider replacing a sleep diary with an automatic measurement, such as a smartwatch, which would not disturb sleep. To obtain accurate results on the evaluation of the possibility of such a replacement, a field study was conducted with a total of 166 overnight recordings, followed by an analysis of the results. In this evaluation, objective sleep measurement with a Samsung Galaxy Watch 4 was compared to a subjective approach with a sleep diary, which is a standard method in sleep medicine. The focus was on comparing four relevant sleep characteristics: falling asleep time, waking up time, total sleep time (TST), and sleep efficiency (SE). After evaluating the results, it was concluded that a smartwatch could replace subjective measurement to determine falling asleep and waking up time, considering some level of inaccuracy. In the case of SE, substitution was also proved to be possible. However, some individual recordings showed a higher discrepancy in results between the two approaches. For its part, the evaluation of the TST measurement currently does not allow us to recommend substituting the measurement method for this sleep parameter. The appropriateness of replacing sleep diary measurement with a smartwatch depends on the acceptable levels of discrepancy. We propose four levels of similarity of results, defining ranges of absolute differences between objective and subjective measurements. By considering the values in the provided table and knowing the required accuracy, it is possible to determine the suitability of substitution in each individual case. The introduction of a “similarity level” parameter increases the adaptability and reusability of study findings in individual practical cases.
Black-box variational inference (BBVI) is a technique to approximate the posterior of Bayesian models by optimization. Similar to MCMC, the user only needs to specify the model; then, the inference procedure is done automatically. In contrast to MCMC, BBVI scales to many observations, is faster for some applications, and can take advantage of highly optimized deep learning frameworks since it can be formulated as a minimization task. In the case of complex posteriors, however, other state-of-the-art BBVI approaches often yield unsatisfactory posterior approximations. This paper presents Bernstein flow variational inference (BF-VI), a robust and easy-to-use method flexible enough to approximate complex multivariate posteriors. BF-VI combines ideas from normalizing flows and Bernstein polynomial-based transformation models. In benchmark experiments, we compare BF-VI solutions with exact posteriors, MCMC solutions, and state-of-the-art BBVI methods, including normalizing flow-based BBVI. We show for low-dimensional models that BF-VI accurately approximates the true posterior; in higher-dimensional models, BF-VI compares favorably against other BBVI methods. Further, using BF-VI, we develop a Bayesian model for the semi-structured melanoma challenge data, combining a CNN model part for image data with an interpretable model part for tabular data, and demonstrate, for the first time, the use of BBVI in semi-structured models.
This policy brief presents the possibilities of using big data analytics for safe, decarbonised and climate-resilient infrastructure. The policy brief focuses on current constraints and limitations to applying big data analytics to the infrastructure ecosystem and presents several examples and best practices for different infrastructure sectors and at different policy levels (national, municipal) to highlight recommendations and policy requirements needed for deep digital transformation and sustainable solutions in infrastructure planning and delivery.
Driver assistance systems are increasingly becoming part of the standard equipment of vehicles and thus contribute to road safety. However, as they become more widespread, the requirements for cost efficiency are also increasing, and so few and inexpensive sensors are used in these systems. Especially in challenging situations, this leads to the fact that target discrimination cannot be ensured which may lead to false reactions of the driver assistance system. In this paper, the Boids flocking algorithm is used to generate semantic neighborhood information between tracked objects which in turn can significantly improve the overall performance. Two different variants were developed: First, a free-moving flock whereby a separate flock is generated per tracked object and second, a formation-controlled flock where boids of a single flock move along the future road course in a pre-defined formation. In the first approach, the interaction between the flocks as well as the interaction between the boids within a flock is used to generate additional information, which in turn can be used to improve, for example, lane change detection. For the latter approach, new behavioral rules have been developed, so that the boids can reliably identify control-relevant objects to a driver assistance system. Finally, the performance of the presented methods is verified through extensive simulations.
Matrix methods for the computation of bounds for the range of a complex polynomial and its modulus over a rectangular region in the complex plane are presented. The approach relies on the expansion of the given polynomial into Bernstein polynomials. The results are extended to multivariate complex polynomials and rational functions.
Deep neural networks have become a veritable alternative to classic speaker recognition and clustering methods in recent years. However, while the speech signal clearly is a time series, and despite the body of literature on the benefits of prosodic (suprasegmental) features, identifying voices has usually not been approached with sequence learning methods. Only recently has a recurrent neural network (RNN) been successfully applied to this task, while the use of convolutional neural networks (CNNs) (that are not able to capture arbitrary time dependencies, unlike RNNs) still prevails. In this paper, we show the effectiveness of RNNs for speaker recognition by improving state of the art speaker clustering performance and robustness on the classic TIMIT benchmark. We provide arguments why RNNs are superior by experimentally showing a “sweet spot” of the segment length for successfully capturing prosodic information that has been theoretically predicted in previous work.
The class of square matrices of order n having a negative determinant and all their minors up to order n-1 nonnegative is considered. A characterization of these matrices is presented which provides an easy test based on the Cauchon algorithm for their recognition. Furthermore, the maximum allowable perturbation of the entry in position (2,2) such that the perturbed matrix remains in this class is given. Finally, it is shown that all matrices lying between two matrices of this class with respect to the checkerboard ordering are contained in this class, too.
Code-based cryptosystems are promising candidates for post-quantum cryptography. Recently, generalized concatenated codes over Gaussian and Eisenstein integers were proposed for those systems. For a channel model with errors of restricted weight, those q-ary codes lead to high error correction capabilities. Hence, these codes achieve high work factors for information set decoding attacks. In this work, we adapt this concept to codes for the weight-one error channel, i.e., a binary channel model where at most one bit-error occurs in each block of m bits. We also propose a low complexity decoding algorithm for the proposed codes. Compared to codes over Gaussian and Eisenstein integers, these codes achieve higher minimum Hamming distances for the dual codes of the inner component codes. This property increases the work factor for a structural attack on concatenated codes leading to higher overall security. For comparable security, the key size for the proposed code construction is significantly smaller than for the classic McEliece scheme based on Goppa codes.
Global agriculture will face major challenges in the future. In addition to the increasing demand for food due to constant population growth, the consequences of climate change will make it even more difficult to operate agriculture and supply people with food. In addition to further productivity increases in traditional agriculture, new concepts for sustainable and scalable food production are needed. Vertical farming offers a promising approach.
The aim of this project is to demonstrate how vertical farming can be used to ensure sustainable food production and how this concept can be applied in the pioneering Maun Science Park project in Botswana. In doing so, the Maun Science Park will address future challenges such as demographics, governance and climate change and become a best practice model for Botswana, the whole of Africa and the world. The country of Botswana grew to become one of the most prosperous countries in Africa in recent decades due to strong economic growth from mining. However, the population faces great challenges in the future; in addition to great social inequality, climate change threatens the country's overall supply.
With the help of a literature review and qualitative and quantitative interviews with stakeholders from Maun (Botswana), the potentials and challenges for vertical farming in Botswana could be identified and future measures for a possible realization could be derived. Basically, some challenges in Botswana are addressed by the technology, for example, Vertical Farming offers high food security through year-round production of food through the closed ecosystem and creates independence from current and future climatic conditions, poor conditions for traditional agriculture (e.g. infertile soils) and foreign imports. However, the main structural problems of agriculture in Botswana, such as the lack of infrastructure, know-how and policy support, are not addressed.
Botswana serves as a role model for other African countries due to its rapid development in recent decades. Since the country is sparsely populated and a large part of the rural population depends on agriculture, especially livestock, this sector forms the backbone of the national economy. The digitization of this sector offers promising opportunities for economic growth and driving Botswana's evolution to a digital economy, while real value is being created for smallholder farmers. To support this process, an ITU research project made the key recommendation for the development of a digital crowdfarming tool and marketplace to create a digital ecosystem for smallholder agriculture. Within the research project, infrastructural challenges such as the creation of rural electricity supply and internet access, as well as the smallholders' need for remote monitoring, management, and better connectivity, were identified.
Based on the findings of the ITU research report, this bachelor's thesis aims to identify potential innovations for the digital development of smallholder agriculture in Botswana and to conceptualize proposals to address the identified challenges and needs of smallholder farmers. To achieve this, solutions were developed through literature research, technology analysis and expert involvement. These included the design of a decentralized mini-grid for power supply, proposals to create internet access, and the graphic visualization of a conceptual app. The latter addresses smallholder farmers' needs for remote monitoring, market access, knowledge enhancement, and connection to colleagues, buyers, and investors.
The proposed solutions and developed concepts provide impulses for further research and can serve as a basis for an extended evaluation through further involvement of experts and stakeholders.
Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis—polysomnography (PSG)—is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.
The actual task of electrocardiographic examinations is to increase the reliability of diagnosing the condition of the heart. Within the framework of this task, an important direction is the solution of the inverse problem of electrocardiography, based on the processing of electrocardiographic signals of multichannel cardio leads at known electrode coordinates in these leads (Titomir et al. Noninvasiv electrocardiotopography, 2003), (Macfarlane et al. Comprehensive Electrocardiology, 2nd ed. (Chapter 9), 2011).
This paper describes the rationale and the development of a structured digital approach for measuring corporate environmental sustainability using performance metrics.
It is impossible to imagine today's age without the preservation of our environment, not even in the corporate environment. Currently, sustainability is mostly only rudimentarily considered in companies, mostly only with written down phrases on the website. This will no longer be sufficient in the future, which is why companies should record sustainability on a numerical basis. Based on the development of a workable concept for companies, a small empirical study was carried out, which can be used to numerically measure the sustainability performance of companies. Two utility analyses were completed.
One of them was supplemented by expert interviews. Well-known practitioners from the business world were interviewed and asked for their assessment of ecological performance indicators. The result of the research is an indicator-based concept that can be applied in corporate practice to determine ecological sustainability performance.
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
Digital federated platforms and data cooperatives for secure, trusted and sovereign data exchange will play a central role in the construction industry of the future. With the help of platforms, cooperatives and their novel value creation, the digital transformation and the degree of organization of the construction value chain can be taken to a new level of collaboration. The goal of this research project was to develop an experimental prototype for a federated innovation data platform along with a suitable exemplary use case. The prototype is to serve the construction industry as a demonstrator for further developments and form the basis for an innovation platform. It exemplifies how an overall concept is concretely implemented along one or more use cases that address high-priority industry pain points. This concept will create a blueprint and a framework for further developments, which will then be further established in the market. The research project illuminates the perspective of various governance innovations to increase industry collaboration, productivity and capital project performance and transparency as well as the overall potential of possible platform business models. However, a comprehensive expert survey revealed that there are considerable obstacles to trust-based data exchange between the key stakeholders in the industry value network. The obstacles to cooperation are predominantly not of a technical nature but rather of a competitive, predominantly trust-related nature. To overcome these obstacles and create a pre-competitive space of trust, the authors therefore propose the governance structure of a data cooperative model, which is discussed in detail in this paper.
Lidar sensors are widely used for environmental perception on autonomous robot vehicles (ARV). The field of view (FOV) of Lidar sensors can be reshaped by positioning plane mirrors in their vicinity. Mirror setups can especially improve the FOV for ground detection of ARVs with 2D-Lidar sensors. This paper presents an overview of several geometric designs and their strengths for certain vehicle types. Additionally, a new and easy-to-implement calibration procedure for setups of 2D-Lidar sensors with mirrors is presented to determine precise mirror orientations and positions, using a single flat calibration object with a pre-aligned simple fiducial marker. Measurement data from a prototype vehicle with a 2D-Lidar with a 2 m range using this new calibration procedure are presented. We show that the calibrated mirror orientations are accurate to less than 0.6° in this short range, which is a significant improvement over the orientation angles taken directly from the CAD. The accuracy of the point cloud data improved, and no significant decrease in distance noise was introduced. We deduced general guidelines for successful calibration setups using our method. In conclusion, a 2D-Lidar sensor and two plane mirrors calibrated with this method are a cost-effective and accurate way for robot engineers to improve the environmental perception of ARVs.
Atom interferometers have a multitude of proposed applications in space including precise measurements of the Earth's gravitational field, in navigation & ranging, and in fundamental physics such as tests of the weak equivalence principle (WEP) and gravitational wave detection. While atom interferometers are realized routinely in ground-based laboratories, current efforts aim at the development of a space compatible design optimized with respect to dimensions, weight, power consumption, mechanical robustness and radiation hardness. In this paper, we present a design of a high-sensitivity differential dual species 85Rb/87Rb atom interferometer for space, including physics package, laser system, electronics and software. The physics package comprises the atom source consisting of dispensers and a 2D magneto-optical trap (MOT), the science chamber with a 3D-MOT, a magnetic trap based on an atom chip and an optical dipole trap (ODT) used for Bose-Einstein condensate (BEC) creation and interferometry, the detection unit, the vacuum system for 10-11 mbar ultra-high vacuum generation, and the high-suppression factor magnetic shielding as well as the thermal control system.
The laser system is based on a hybrid approach using fiber-based telecom components and high-power laser diode technology and includes all laser sources for 2D-MOT, 3D-MOT, ODT, interferometry and detection. Manipulation and switching of the laser beams is carried out on an optical bench using Zerodur bonding technology. The instrument consists of 9 units with an overall mass of 221 kg, an average power consumption of 608 W (819 W peak), and a volume of 470 liters which would well fit on a satellite to be launched with a Soyuz rocket, as system studies have shown.
This thesis deals with background, theory, design, layout and experimental test results of an analogue CMOS VLSI current-mode analog-to-digital converter. This system supports a project, whose goal it is to build a biologically relevant model of synaptic plasticity, named the Artificial Synapse. A critical part of the design, which is based on analogue CMOS VLSI circuits, is the ability to activate a discrete number of channels by sampling an analogue signal. Since currents are the signal of interest and transistors are biased in weak inversion (subthreshold regime), the system requires a current mode A/D circuit that it can operate at ultra-low power and current levels. To meet this need, two new innovative A/D converter approaches are proposed to replace the system’s previous A/D converter design which suffered from a non-linear resolution, uncoded output code and heavy bit oscillations. The initial technical requirements and key criteria for the new converter comprise a resolution of one nano ampere, an input current range between 0 – 100nA, conversion frequencies of up to 5kHz, and a power supply voltage of less than 1.5V. Temperature range, space occupation and power dissipation aspects were not specified due to the early stage of the related Artificial Synapse project. The novel converters both produce seven bit thermometer codes, their functional principle can be best described as current mode flash analog-to-digital converters (ADCs). Due to the fact that the input signal is in the area of a subthreshold current, it is selfevident that the A/D converter design should operate at a subthreshold realm. To support low power operation, clocks or high currents could not be used and were excluded from the design from the very start. To encode the thermometer code into standard binary code, a seven-to-three encoder was designed and integrated on the chip. In October 2003, the design was submitted for production to the MOSIS circuit fabrication service. The AMI Semiconductor 1.5 micron ABN CMOS process was chosen to manufacture the chip. When it was returned in January 2004, simulation results showed that both new A/D converter approaches accomplished excellent results which were expected from SPICE simulation results. With the new chip installed, it became possible to resolve input currents as small as one nano ampere and achieve conversion frequencies of up to 5kHz. The circuits also both meet the requirements which were set at the beginning of the project to operate at a power supply voltage of less than 1.5V, processing input currents in the range between 0 – 100nA. A prototype printed circuit board (PCB) was developed, produced and employed for experiments with the chip. The major application of this test-bed is the ability to generate and measure extremely low currents with high precision. This enables the monitoring of the very small currents that are processed by the chip.
Botswana is a country in southern Africa with rich mineral resources, which has built its economy on mining. Due to challenges in the upcoming years caused by climate and demographic change, it aims to move away from a resource-based economy to a knowledge-based economy in the long term. In order to support the
process, the Maun Science Park, a centre for research and development is planned to be created in Maun, a town on the edge of the Okavango Delta. The project is initiated by the “International Resilience and Sustainability Partnership” (inRES), a non-governmental organization. The project is currently in the initiation phase.
The purpose of this thesis is to determine a cost framework with exemplary developer calculation and sensitivity analysis for the Maun Science Park Project in Botswana. Therefor, a source research was performed in a first step. Based on this, interviews were conducted with members of the inRES. Based on the data
obtained and further assumptions, a cost framework for the different project phases of the MSP project was established. Subsequently, a developer calculation
was exemplarily carried out on the basis of the project phase 2 and a sensitivity analysis was performed.
During the interviews, data was collected on the different project phases. It became clear that the interview partners had partly inconsistent perceptions
about different project phases. The calculation can be used as a basis for further calculation at the time of concretization of the planning data.
In Maun, Botswana, a self-sufficient, sustainable and future-oriented district will be created, the Maun Science Park. Within this project, several 5-8 storey smart homes shall be built in sustainable construction. The aim of this thesis is to develop a sustainable structural concept for those homes of the Maun Science Park. In a first step, the general basics for tall building structures and sustainable construction were established. Based on those fundamentals, criteria for the structural requirements, the ecological as well as the social sustainability of a structural design could be defined. Subsequently, four structural systems were drafted: a concrete core structure, a steel shear frame structure, a rammed earth shear wall structure and a wooden diagrid structure. In addition to the pre-dimensioning of the systems, a life cycle assessment was set up to evaluate the ecological sustainability of the designs. With the help of a utility value analysis, the wooden diagrid structure was determined as the preferred variant. The comparison of the designs also allows to draw general conclusions for the development of sustainable tall building structures. The results of the life cycle assessment show the advantage of wood as an ecological building material over industrially manufactured building materials, such as steel and concrete. Whereas rammed earth, a likewise ecological building material, is not convincing due to its low strength. In general, a balance is created in the life cycle assessment between ecological and industrially manufactured products in regard of strength and environmental impact. In terms of social sustainability, the design of the structure system can significantly influence the flexibility and use of local resources. However, due to the diversity of sustainable construction, the development of a structural system should be linked to an overarching sustainability concept that takes architecture and stakeholders into account.
Cities around the world are facing an increasing number of global and local challenges, such as climate change and scarcity of raw materials. At the same time trends like digitalization, globalization and networking gain in importance. For this reason, cities have started imple-menting smart solutions within the urban structure in order to evolve towards a Smart City. In Botswana, the Maun Science Park is intended to provide a best practice approach for a Bot-swanan Smart City. Since Smart City concepts have to be specifically tailored to local condi-tions, the first main goal of this thesis is to develop a synthesis concept for the Maun Science Park. A key problem in cities is the utilization of space, which is further intensified by increasing urbanization and population growth. Therefore, the second main goal is to develop approaches of (digitally) re-programmable space to use available areas intelligently and optimized.
Within the thesis, human-centered design has been applied as structure-giving methodology. By clarifying relevant Smart City contents, considering reference examples as well as identify-ing local challenges and requirements, an appropriate concept has been developed with hu-man-focus. Furthermore, the methodologies of literature research and expert interviews have been used as input in the individual human-centered design phases. In combination with an innovation funnel, the methodology human-centered design forms the structure of the thesis.
In total, ten main solution areas and 37 sub-segments have been identified for the synthesis concept of Maun Science Park. Additionally, a concept for Smart Buildings has been devel-oped as a part of the synthesis concept and as an essential infrastructure component of the Maun Science Park (three main segments, 16 sub-segments). Based on expert input, a priori-tization has been determined by evaluating the impact and economic affordability of the indi-vidual sub-areas. Moreover, individual key areas have been highlighted by identifying direct interactions between sub-segments and on the basis of expert input – these are particularly related to the segments Smart Data and Smart People. Besides the synthesis concept, ap-proaches of (digitally) re-programmable space have been created. Thereby, ten approaches refer to the conversion, reuse or expansion abilities of space within daily, weekly or life cycle. In addition, the conventional (digitally) re-programmable space idea has been extended by two new considerations – “multi-purpose use of built-up space” and “concept programming in the planning phase”. Finally, within an overall consideration – synthesis concept combined with approaches of (digitally) re-programmable space – the added value of the developed contents has been outlined, positive and negative aspects have been identified within a SWOT analysis and the business model of the Maun Science Park approach has been verified in a Business Model Canvas.
Through explicit elaboration, classification and prioritization of solution areas, the developed concept can serve as a basis for further project steps. Based on the defined requirements of the sub-segments, solutions can be developed with regard to the entire Smart City context.
This paper examines how varying antidumping methodologies applied within the World Trade Organization differ in the extent to which they reduce targeted exports. We show that antidumping duties, on average, hit Chinese exporters harder than those of other targeted countries. This difference can be traced back in part to China's non-market economy status, which affects the way antidumping duties are calculated. Furthermore, we show that the type of imposed duty matters, as ad-valorem duties affect exports differently compared to specific duties or duties conditional on the export price. Overall, however, antidumping duties remain effective in reducing imports independent of market economy status.
Sustainable technologies are being increasingly used in various areas of human life. While they have a multitude of benefits, they are especially useful in health monitoring, especially for certain groups of people, such as the elderly. However, there are still several issues that need to be addressed before its use becomes widespread. This work aims to clarify the aspects that are of great importance for increasing the acceptance of the use of this type of technology in the elderly. In addition, we aim to clarify whether the technologies that are already available are able to ensure acceptable accuracy and whether they could replace some of the manual approaches that are currently being used. A two-week study with people 65 years of age and over was conducted to address the questions posed here, and the results were evaluated. It was demonstrated that simplicity of use and automatic functioning play a crucial role. It was also concluded that technology cannot yet completely replace traditional methods such as questionnaires in some areas. Although the technologies that were tested were classified as being “easy to use”, the elderly population in the current study indicated that they were not sure that they would use these technologies regularly in the long term because the added value is not always clear, among other issues. Therefore, awareness-raising must take place in parallel with the development of technologies and services.
Positive systems play an important role in systems and control theory and have found applications in multiagent systems, neural networks, systems biology, and more. Positive systems map the nonnegative orthant to itself (and also the non-positive orthant to itself). In other words, they map the set of vectors with zero sign variation to itself. In this article, discrete-time linear systems that map the set of vectors with up to k-1 sign variations to itself are introduced. For the special case k = 1 these reduce to discrete-time positive linear systems. Properties of these systems are analyzed using tools from the theory of sign-regular matrices. In particular, it is shown that almost every solution of such systems converges to the set of vectors with up to k-1 sign variations. It is also shown that these systems induce a positive dynamics of k-dimensional parallelotopes.
Earthquake response spectra as defined by Eurocode 8 (German NAD) are restricted to soils with shear wave velocities greater than 150 m/s. For soft soil layers e.g. of clay underlain by bedrock special investigations are required because resonance effects of the layer significantly influence the shape of the spectrum. Numerical investigations are normally based on a one-dimensional theory of horizontally polarized shear waves propagating in vertical direction. The paper describes a parametric study to define acceleration response spectra for a soft soil over a half-space for a wide range of soil layer heights and material parameters. Based on this study a simplified method to describe response spectra for the model of a soft soil layer underlain by a viscoelastic halfspace is given.
Fatigue and drowsiness are responsible for a significant percentage of road traffic accidents. There are several approaches to monitor the driver’s drowsiness, ranging from the driver’s steering behavior to analysis of the driver, e.g. eye tracking, blinking, yawning or electrocardiogram (ECG). This paper describes the development of a low-cost ECG sensor to derive heart rate variability (HRV) data for the drowsiness detection. The work includes the hardware and the software design. The hardware has been implemented on a printed circuit board (PCB) designed so that the board can be used as an extension shield for an Arduino. The PCB contains a double, inverted ECG channel including low-pass filtering and provides two analog outputs to the Arduino, that combined them and performs the analog-to-digital conversion. The digital ECG signal is transferred to an NVidia embedded PC where the processing takes place, including QRS-complex, heart rate and HRV detection as well as visualization features. The compact resulting sensor provides good results in the extraction of the main ECG parameters. The sensor is being used in a larger frame, where facial-recognition-based drowsiness detection is combined with ECG-based detection to improve the recognition rate under unfavorable light or occlusion conditions.
For decades now, exports and import have grown more rapidly than domestic production. This is a strong indication that, besides the rapid growth of foreign trade in final goods, trade in intermediates is becoming increasingly important. For this reason, an input-output ap-proach is more appropriate for any analysis of diversification than a traditional approach based purely on macroeconomic data.
This article analyses economic diversification in Gulf Cooperation Council (GCC) countries using data from input-output tables which are an integral part of the national accounts. We compare the performance of the GCC economies with that of a reference case, Norway, which is considered to have successfully diversified its economy despite having a large oil resource base. It also assesses these countries’ relative progress on sustainable development using a measure of the World Bank, adjusted net savings, which evaluates the true rate of savings in an economy after accounting for investments in physical and human capital, de-pletion of natural resources, and damage from environmental pollution.
The article concludes that GCC countries have, contrary to expectation, collectively per-formed relatively well on diversification, but their performance on sustainable development varies.
There was hardly another development which influenced the life on earth as much as the development of the communication technology in the last decades. The advantages of mobile communication brought the branch enormeous growth rates. However, for some years an increasing saturation has been looming in the markets especially in the developed nations and new marketing strategies are needed in order for companies to be able to distance themselves from their competitors. Against the background of this situation ICT companies all over the world started to look for new growth opportunities and found them in the so called “emerging markets” of the developing nations. To exploit this potential will be the one central challenge for the mobile communication industry for the next years. With this book I want to direct the gaze of all readers towards these markets which hold an enormous potential for the whole industry. Furthermore, I want to introduce some generic strategic approaches which can help firms to successfully participate in these markets.
Purpose
The goal of this research survey was to propose an entrepreneurship education model for students in higher education institutions.
Methodology
A questionnaire was distributed to 246 randomly sampled students at the Universitas Negeri Jakarta. The data was analyzed through Structural Equation Modeling to study the variables of entrepreneurship education for higher education students and examine whether it can be predicted by the university leadership as a facilitator of entrepreneurial culture, university departments as promoters of entrepreneurial skills, and university research as an incubator of local business
development.
Findings
The results show that university leadership as a facilitator of entrepreneurial culture is supported by the university leadership’s fostering a culture of entrepreneurial thinking. It was also evident that the university placed sufficient emphasis on entrepreneurial education, and it successfully motivated lecturers to embrace entrepreneurship education, and students to embrace entrepreneurship education. The results also indicated that university departments acted as promoters of entrepreneurial skills and stimulated students to attain sufficient entrepreneurial skills during their university education. Lastly, the university research also proved as an incubator of local business development and was found influenced by the university conducting research projects with local
private sector businesses and supporting graduates planning to launch start-ups.
Implications to Research and Practice
The survey results will provide valuable policy insights to improve entrepreneurship education. The university faculty and students would have opportunities to gain practical experience in local private sector businesses. The model of entrepreneurship education proposed herein can be applied for higher education students.
The growing error rates of triple-level cell (TLC) and quadruple-level cell (QLC) NAND flash memories have led to the application of error correction coding with soft-input decoding techniques in flash-based storage systems. Typically, flash memory is organized in pages where the individual bits per cell are assigned to different pages and different codewords of the error-correcting code. This page-wise encoding minimizes the read latency with hard-input decoding. To increase the decoding capability, soft-input decoding is used eventually due to the aging of the cells. This soft-decoding requires multiple read operations. Hence, the soft-read operations reduce the achievable throughput, and increase the read latency and power consumption. In this work, we investigate a different encoding and decoding approach that improves the error correction performance without increasing the number of reference voltages. We consider TLC and QLC flashes where all bits are jointly encoded using a Gray labeling. This cell-wise encoding improves the achievable channel capacity compared with independent page-wise encoding. Errors with cell-wise read operations typically result in a single erroneous bit per cell. We present a coding approach based on generalized concatenated codes that utilizes this property.
Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems tractable. Here, we present deeptrafo, a package for fitting flexible regression models for conditional distributions using a tensorflow backend with numerous additional processors, such as neural networks, penalties, and smoothing splines. Package deeptrafo implements deep conditional transformation models (DCTMs) for binary, ordinal, count, survival, continuous, and time series responses, potentially with uninformative censoring. Unlike other available methods, DCTMs do not assume a parametric family of distributions for the response. Further, the data analyst may trade off interpretability and flexibility by supplying custom neural network architectures and smoothers for each term in an intuitive formula interface. We demonstrate how to set up, fit, and work with DCTMs for several response types. We further showcase how to construct ensembles of these models, evaluate models using inbuilt cross-validation, and use other convenience functions for DCTMs in several applications. Lastly, we discuss DCTMs in light of other approaches to regression with non-tabular data.
In the reverse engineering process one has to classify parts of point clouds with the correct type of geometric primitive. Features based on different geometric properties like point relations, normals, and curvature information can be used, to train classifiers like Support Vector Machines (SVM). These geometric features are estimated in the local neighborhood of a point of the point cloud. The multitude of different features makes an in-depth comparison necessary. In this work we evaluate 23 features for the classification of geometric primitives in point clouds. Their performance is evaluated on SVMs when used to classify geometric primitives in simulated and real laser scanned point clouds. We also introduce a normalization of point cloud density to improve classification generalization.
Many countries offer state credit guarantees to support credit-constrained exporters. The policy instrument is commonly justified by governments as a means to mitigating adverse outcomes of financial market frictions for exporting firms. Accumulated returns to the German state credit guarantee scheme deriving from risk-compensating premia have outweighed accumulated losses over the past 60 years. Why do private financial agents not step in and provide insurance given that the state-run program yields positive returns? We argue that costs of risk diversification, liquidity management, and coordination among creditors limit the ability of private financial agents to offer comparable insurance products. Moreover, we suggest that the government’s greater effectiveness in recovering claims in foreign countries endows the state with a cost advantage in dealing with the risks involved in large export projects. We test these hypotheses using monthly firm-level data combined with official transaction-level data on covered exports of German firms and find suggestive evidence that positive effects on trade are due to mitigated financial constraints: State credit guarantees benefit firms that are dependent on external finance, if the value at risk which they seek to cover is large, and at times when refinancing conditions on the private financial market are tight.
This thesis investigates methods for the recognition of facial expressions using support vector machines. Rather than trying to recognize facial actions in the face such as raised eyebrow, mouth open and frowns. These facial actions are described in the Facial Action Coding System (FACS) and are essential facial components, which can be combined to form facial expressions. We perform independent recognition of 6 upper and 10 lower action units in the face, which may occur either individually or in combination. Based on a feature extraction from grey-level values, the system is expected to recognize under real-time conditions. Results are presented with different image resolutions, SVM kernels and variations of low-level features.
Image novelty detection is a repeating task in computer vision and describes the detection of anomalous images based on a training dataset consisting solely of normal reference data. It has been found that, in particular, neural networks are well-suited for the task. Our approach first transforms the training and test images into ensembles of patches, which enables the assessment of mean-shifts between normal data and outliers. As mean-shifts are only detectable when the outlier ensemble and inlier distribution are spatially separate from each other, a rich feature space, such as a pre-trained neural network, needs to be chosen to represent the extracted patches. For mean-shift estimation, the Hotelling T2 test is used. The size of the patches turned out to be a crucial hyperparameter that needs additional domain knowledge about the spatial size of the expected anomalies (local vs. global). This also affects model selection and the chosen feature space, as commonly used Convolutional Neural Networks or Vision Image Transformers have very different receptive field sizes. To showcase the state-of-the-art capabilities of our approach, we compare results with classical and deep learning methods on the popular dataset CIFAR-10, and demonstrate its real-world applicability in a large-scale industrial inspection scenario using the MVTec dataset. Because of the inexpensive design, our method can be implemented by a single additional 2D-convolution and pooling layer and allows particularly fast prediction times while being very data-efficient.
In this paper, a novel feature-based sampling strategy for nonlinear Model Predictive Path Integral (MPPI) control is presented. Using the MPPI approach, the optimal feedback control is calculated by solving a stochastic optimal control (OCP) problem online by evaluating the weighted inference of sampled stochastic trajectories. While the MPPI algorithm can be excellently parallelized, the closed-loop performance strongly depends on the information quality of the sampled trajectories. To draw samples, a proposal density is used. The solver’s and thus, the controller’s performance is of high quality if the sampled trajectories drawn from this proposal density are located in low-cost regions of state-space. In classical MPPI control, the explored state-space is strongly constrained by assumptions that refer to the control value’s covariance matrix, which are necessary for transforming the stochastic Hamilton–Jacobi–Bellman (HJB) equation into a linear second-order partial differential equation. To achieve excellent performance even with discontinuous cost functions, in this novel approach, knowledge-based features are introduced to constitute the proposal density and thus the low-cost region of state-space for exploration. This paper addresses the question of how the performance of the MPPI algorithm can be improved using a feature-based mixture of base densities. Furthermore, the developed algorithm is applied to an autonomous vessel that follows a track and concurrently avoids collisions using an emergency braking feature. Therefore, the presented feature-based MPPI algorithm is applied and analyzed in both simulation and full-scale experiments.
Twenty-first century infrastructure needs to respond to changing demographics, becoming climate neutral, resilient, and economically affordable, while remaining a driver for development and shared prosperity. However, the infrastructure sector remains one of the least innovative and digitalized, plagued by delays, cost overruns, and benefit shortfalls. The authors assessed trends and barriers in the planning and delivery of infrastructure based on secondary research, qualitative
interviews with internationally leading experts, and expert workshops. The analysis concludes that the root-cause of the industry’s problems is the prevailing fragmentation of the infrastructure value chain and a lacking long-term vision for infrastructure. To help overcome these challenges, an integration of the value chain is needed. The authors propose that this could be achieved through a use-case-based, as well as vision and governance-driven creation of federated digital platforms applied to infrastructure projects and outline a concept. Digital platforms enable full-lifecycle participation and responsible governance guided by a shared infrastructure vision. This paper has contributed as policy recommendation to the Group of Twenty (G20) in 2021.
Botswana, a new construction project – the Maun Science Park - is to be built with a focus on sustainability and to create a new living space for the rapidly growing population in Africa. The project will be a blueprint for future projects in Africain terms of progress, technology and sustainability. This thesis will deal with its financial framework and will serve as a basis for the development of ways and means of financing such projects.
Today’s markets are characterized by fast and radical changes, posing an essential challenge to established companies. Startups, yet, seem to be more capable in developing radical innovations to succeed in those volatile markets. Thus, established companies started to experiment with various approaches to implement startup-like structures in their organization. Internal corporate accelerators (ICAs) are a novel form of corporate venturing, aiming to foster bottom-up innovations through intrapreneurship. However, ICAs still lack empirical investigations. This work contributes to a deeper understanding of the interface between the ICA and the core organization and the respective support activities (resource access and support services) that create an innovation-supportive work environment for the intrapreneurial team. The results of this qualitative study, comprising 12 interviews with ICA teams out of two German high-tech companies, show that the resources provided by ICAs differ from the support activities of external accelerators. Further, the study shows that some resources show both supportive as well as obstructive potential for the intrapreneurial teams within the ICA.
Further applications of the Cauchon algorithm to rank determination and bidiagonal factorization
(2018)
For a class of matrices connected with Cauchon diagrams, Cauchon matrices, and the Cauchon algorithm, a method for determining the rank, and for checking a set of consecutive row (or column) vectors for linear independence is presented. Cauchon diagrams are also linked to the elementary bidiagonal factorization of a matrix and to certain types of rank conditions associated with submatrices called descending rank conditions.
In this article, the collection of classes of matrices presented in [J. Garloff, M. Adm, ad J. Titi, A survey of classes of matrices possessing the interval property and related properties, Reliab. Comput. 22:1-14, 2016] is continued. That is, given an interval of matrices with respect to a certain partial order, it is desired to know whether a special property of the entire matrix interval can be inferred from some of its element matrices lying on the vertices of the matrix interval. The interval property of some matrix classes found in the literature is presented, and the interval property of further matrix classes including the ultrametric, the conditionally positive semidefinite, and the infinitely divisible matrices is given for the first time. For the inverse M-matrices the cardinality of the required set of vertex matrices known so far is significantly reduced.
Generalized Concatenated Codes over Gaussian and Eisenstein Integers for Code-Based Cryptography
(2021)
The code-based McEliece and Niederreiter cryptosystems are promising candidates for post-quantum public-key encryption. Recently, q-ary concatenated codes over Gaussian integers were proposed for the McEliece cryptosystem together with the one-Mannheim error channel, where the error values are limited to Mannheim weight one. Due to the limited error values, the codes over Gaussian integers achieve a higher error correction capability than maximum distance separable (MDS) codes with bounded minimum distance decoding. This higher error correction capability improves the work factor regarding decoding attacks based on information-set decoding. The codes also enable a low complexity decoding algorithm for decoding beyond the guaranteed error correction capability. In this work, we extend this coding scheme to codes over Eisenstein integers. These codes have advantages for the Niederreiter system. Additionally, we propose an improved code construction based on generalized concatenated codes. These codes extent the rate region where the work factor is beneficial compared to MDS codes. Moreover, generalized concatenated codes are more robust against structural attacks than ordinary concatenated codes.
The transformation to an Industry 4.0, which is in general seen as a solution to increasing market challenges, is forcing companies to radically change their way of thinking and to be open to new forms of cooperation. In this context, the opening-up of the innovation process is widely seen as a necessity to meet these challenges, especially for small and medium enterprises (SMEs). The aim of the study therefore is to analyze how cooperation today can be characterized, how this character has changed since the establishment of the term Industry 4.0 at Hanover Fair in 2011 and which cooperation strategies have proven successful. The analysis consists of a quantitative, secondary data analysis that includes country-specific data from 35 European countries of 2010 and 2016 collected by the European Commission and the OECD. The research, focusing on the secondary sector, shows that multinational enterprises MNEs still tend to cooperate more than SMEs, with a slight overall trend towards protectionism. Nevertheless, there is a clear tendency towards the opening-up of SMEs. In this regard, especially universities, competitors and suppliers have become increasingly attractive as cooperation partners for SMEs.
With the increasing challenges of the 21st century, such as a rapidly growing population, increasing hunger and the destruction of the environment, the demand for sustainable and future-oriented ways of living is growing. To meet this demand, a residential district named Maun Science Park is being built in Botswana to develop a resilient society. In addition to the application of modern technology to optimise the use of resources, the environmentally friendly construction of the buildings is another goal of the project. This thesis investigates the prefabrication of rammed earth in terms of implementation and profitability for the Maun Science Park.
For this purpose, the specific properties, handling, as well as the application of the building material in prefabrication are first discussed.
This is followed by an investigation of how the work processes of prefabrication can be implemented in the Maun Science Park. Based on this, a profitability test is carried out using a break-even and sensitivity analysis.
The analyses showed that the investment in prefabrication is not profitable within the assumed production volume, which is due to the high fixed costs. These are primarily generated by the two main cost drivers, consisting of the new construction of the production hall and the rental of heavy construction equipment.
Lastly, recommendations for action were formulated that provide for a cost reduction in both the two main cost drivers as well as for other decisive factors.
Throughout this thesis, the implementation of tools for knowledge management as a key factor for sustainable corporate development, is presented. In industries with a high fluc-tuation rate, such as construction, efficient knowledge management is of particular im-portance. Companies feel the effects of negligent handling of this resource especially dur-ing the Corona pandemic. Restructuring leads to experienced employees leaving the com-pany – and with them the know-how and experience gained. With a systematic knowledge transfer, the most important insights in such situations remain within the or-ganization. Thus, the company becomes crisis-proof and receives all the tools it needs to grow healthily again after the recession. Practical data from competitors indicates that knowledge management promises savings potential of several million euros per year for BAM. Further potentials in the areas of sustainability, customer- and employee satisfac-tion as well as occupational safety, which do not lead to savings, are also worth mention-ing. This thesis determines the current maturity level of knowledge management at BAM, before introducing processes and systems that successively drive the improvement. The developed methods simultaneously help to prevent and solve problems and systematical-ly promote the continuous improvement of all work processes in the company.
Detailed steps are presented to carry out change management towards the successful introduction and further development of knowledge management at BAM. A major focus is on interpersonal factors. The related topic of knowledge culture was recently ranked by german think-tank Zukunftsinstitut as one of the top 5 megatrends for companies in the 2020s. The methods developed, contribute to the creation of such a culture and to the transformation of BAM towards a learning organization. Knowledge management identi-fies with the BAM values. In the course of this thesis it will be shown how the system by its very nature, helps to implement these values in the work of every employee.
The results of this elaboration were recently awarded the Digital Construction Award 2020 for Business Excellence at BAM Deutschland AG.
Cities around the world are facing the implications of a changing climate as an increasingly pressing issue. The negative effects of climate change are already being felt today. Therefore, adaptation to these changes is a mission that every city must master. Leading practices worldwide demonstrate various urban efforts on climate change adaptation (CCA) which are already underway. Above all, the integration of climate data, remote sensing, and in situ data is key to a successful and measurable adaptation strategy. Furthermore, these data can act as a timely decision support tool for municipalities to develop an adaptation strategy, decide which actions to prioritize, and gain the necessary buy-in from local policymakers. The implementation of agile data workflows can facilitate the integration of climate data into climate-resilient urban planning. Due to local specificities, (supra)national, regional, and municipal policies and (by) laws, as well as geographic and related climatic differences worldwide, there is no single path to climate-resilient urban planning. Agile data workflows can support interdepartmental collaboration and, therefore, need to be integrated into existing management processes and government structures. Agile management, which has its origins in software development, can be a way to break down traditional management practices, such as static waterfall models and sluggish stage-gate processes, and enable an increased level of flexibility and agility required when urgent. This paper presents the findings of an empirical case study conducted in cooperation with the City of Constance in southern Germany, which is pursuing a transdisciplinary and trans-sectoral co-development approach to make management processes more agile in the context of climate change adaptation. The aim is to present a possible way of integrating climate data into CCA planning by changing the management approach and implementing a toolbox for low-threshold access to climate data. The city administration, in collaboration with the University of Applied Sciences Constance, the Climate Service Center Germany (GERICS), and the University of Stuttgart, developed a co-creative and participatory project, CoKLIMAx, with the objective of integrating climate data into administrative processes in the form of a toolbox. One key element of CoKLIMAx is the involvement of the population, the city administration, and political decision-makers through targeted communication and regular feedback loops among all involved departments and stakeholder groups. Based on the results of a survey of 72 administrative staff members and a literature review on agile management in municipalities and city administrations, recommendations on a workflow and communication structure for cross-departmental strategies for resilient urban planning in the City of Constance were developed.
InBetween
(2017)
Increasing robustness of handwriting recognition using character N-Gram decoding on large lexica
(2016)
Offline handwriting recognition systems often include a decoding step, that is retrieving the most likely character sequence from the underlying machine learning algorithm. Decoding is sensitive to ranges of weakly predicted characters, caused e.g. by obstructions in the scanned document. We present a new algorithm for robust decoding of handwriting recognizer outputs using character n-grams. Multidimensional hierarchical subsampling artificial neural networks with Long-Short-Term-Memory cells have been successfully applied to offline handwriting recognition. Output activations from such networks, trained with Connectionist Temporal Classification, can be decoded with several different algorithms in order to retrieve the most likely literal string that it represents. We present a new algorithm for decoding the network output while restricting the possible strings to a large lexicon. The index used for this work is an n-gram index with tri-grams used for experimental comparisons. N-grams are extracted from the network output using a backtracking algorithm and each n-gram assigned a mean probability. The decoding result is obtained by intersecting the n-gram hit lists while calculating the total probability for each matched lexicon entry. We conclude with an experimental comparison of different decoding algorithms on a large lexicon.
In order to elaborate inflation and deflation tendencies due to the COVID-19 pandemic and how they are tried to be actively influenced, this paper compares news regarding the measurements of central banks in Europe, USA and Japan. Factors affecting inflation are defined in conjunction with the typical measurements of central banks and conclusions are drawn in respect to differences of the most recent correcting behavior. The paper is concluded by discussing how price levels might develop during and after the crisis.
InnoCrowd, a Product Classification System for Design Decision in a Crowdsourced Product Innovation
(2021)
System engineering focuses on how to design and manage complex systems. Meanwhile, in the era of Industry 4.0 and Internet of Things (IoT), systems are getting more complex. Contributors to higher complexity include the usage of modern components (e.g. mechatronics), new manufacturing technologies (e.g. 3D Print) and new engineering product development processes, e.g. open innovation. Open innovation is enabled by IoT, where people and devices are easily connected, and it supports development of more innovative products through ideas gained from predecessors and collaborators world wide. Some researchers suggest this approach is up to three times faster and five times cheaper than conventional approaches [Gassmann, 2012], [Howe, 2008], [Kusumah, 2018]. Because open innovation is relatively new, many managers do not know how to employ it effectively in some phases of product development [Schenk, 2009], [Afuah, 2017], including requirements definition, design and engineering processes (task assignment) through quality assurance. Also, they have trouble estimating and controlling development time and cost [Nevo, 2020], [Thanh, 2015]. As a consequence, the acceptance of this new approach in the industry is limited. Research activities addressing this new approach mainly address high-level and qualitive issues. Few effective methods are available to estimate project risk and to decide whether to initiate a project.
We propose InnoCrowd, a decision support system that uses an improved method to support these tasks and make decisions about crowdsourced engineering product development.
InnoCrowd uses natural language processing and machine learning to build a knowledgebase of crowdsourced product developments. InnoCrowd presents a manager with results of similar projects to show which practices led to good results. A manager of a new project can use this guidance to employ best practices for product requirements definition, project schedule, and other aspects, thereby reducing risk and increasing chances for success.
Thermal shape memory alloys show extraordinary material properties and can be used as actuators, dampers and sensors. Since their discovery in the middle of the last century they have been investigated and further developed. The majority of the industrial applications with the highest material sales can still be found in the medical industry, where they are used due to their superelastic and thermal shape memory effect, e.g. as stents or as guidewires and tools in the minimal invasive surgery. Particularly in recent years, more and more applications have been developed for other industrial fields, e.g. for the household goods, civil engineering and automotive sector. In this context it is worth mentioning that for the latter sector, million seller series applications have found their way into some European automobile manufacturers. The German VDI guideline for shape memory alloys introduced in 2017 will give the material a further boost in application. Last but not least the new production technologies of additive manufacturing with metal laser sintering plants open up additional applications for these multifunctional materials. This paper gives an overview of the extraordinary material properties of shape memory components, shows examples of different applications and discusses European trends against the background of the most recent standard and new production technologies.
This working paper is part of a PhD research project dealing with the topics Social Responsibility, Stakeholder Theory and Network Governance, run by Maud Schmiedeknecht and supervised by Prof. Dr. habil. Josef Wieland, both from the Konstanz Institute for Intercultural Management, Values and Communication at the Konstanz University of Applied Sciences.
The focus of this part of the research project lies on the process of developing a Social Responsibility Standard within a network made up of various stakeholders. The International Organization for Standardization (ISO) is known as the world´s leading institution for the development of standards. Besides setting standards in the fields of e.g. construction, agriculture and information technology, recently the Technical Management Board (TMB) of ISO proposed to further extend its activities by developing an international standard addressing the social responsibility of organizations. In 2004, a new Working Group was established as a multi-stakeholder group comprised of experts, who are nominated by ISO´s members as well as interested international and regional organizations in order to provide for guidance in setting international standards on social responsibility. In May 2006, the survey was conducted during the third conference of the ISO Working Group in Lisbon, Portugal. This particular empirical study has been designed on the one hand to investigate the motivation of organizations and their delegates to engage in social responsibility. On the other hand, the survey had the objective to evaluate the individual participants' current perception and assessment of the network´s efficiency, effectiveness and legitimacy, a so-called 'snap-shot' of this ISO process1. Overall, the empirical study shows that the organizations and their delegates, who have dealt with the topic SR for several years for diverse reasons, expect a tremendous effect by implementing ISO 26000 in their own organizations. Furthermore, the majority of respondents assess the decision-making process positively within the ISO process with respect to the criteria inclusive, fair, capacity building, legitimate and transparent. Difficulties concerning the distribution of stakeholder influences are being addressed. The results of the survey support the efforts to establish policies and procedures in order to encourage a balanced representation of stakeholders in terms of gender, geographic and stakeholder groups.
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass. It estimates a distribution over the number of clusters k, and for each 1≤k≤kmax, a distribution over the individual cluster assignment for each data point. The network is trained in advance in a supervised fashion on separate data to learn grouping by any perceptual similarity criterion based on pairwise labels (same/different group). It can then be applied to different data containing different groups. We demonstrate promising performance on high-dimensional data like images (COIL-100) and speech (TIMIT). We call this “learning to cluster” and show its conceptual difference to deep metric learning, semi-supervise clustering and other related approaches while having the advantage of performing learnable clustering fully end-to-end.
Anthropologists’ arrival stories have long served to justify, naturalize, and domesticate—often through humor—the fraught moment of entering unasked into other people's lives. This textual convention has been thoroughly critiqued, but no comparable attention has been paid to the analogous moment of departure from the field. The digital age enables both sides to maintain contact, a shift that negates the finality of earlier departures. This article engages the changes wrought by digital media that allow us to remain connected to the field. While this seems a humane affordance, it also means that it is no longer feasible to cleanly sever ties established ‘there’. When anthropologists leave the field, the field will likely follow them—on Facebook or Instagram.
Digitization and sustainability are the two big topics of our current time. As the usage of digital products like IoT devices continues to grow, it affects the energy consumption caused by the Internet. At the same time, more and more companies feel the need to become carbon neutral and sustainable. Determining the environmental impact of an IoT device is challenging, as the production of the hardware components should be considered and the electricity consumption of the Internet since this is the primary communication medium of an IoT device. Estimating the electricity consumption of the Internet itself is a complex task. We performed a life cycle assessment (LCA) to determine the environmental impact of an intelligent smoke detector sold in Germany, taking its whole life-cycle from cradle-to-grave into account. We applied the impact assessment method ReCiPe 2016 Midpoint and compared its results with ILCD 2011 Midpoint+ to check the robustness of our results. The LCA results showed that electricity consumption during the use phase is the main contributor to environmental impacts. The mining of coal causes this contribution, which is a part of the German electricity mix. Consequently, the smoke detector mainly contributes to the impact categories of freshwater and marine ecotoxicity, but only marginally to global warming.
CO2 compensation measures, in particular the compensation of flights, are becoming more and more popular. Carbon offsetting is defined as measures financed by donations that save greenhouse gases previously emitted elsewhere through climate protection projects.
CO2 abatement costs are often low in developing countries. This is why most offset projects are implemented there. Nevertheless, this does not mean that the holiday resort and the project country are in any way related to each other.
By linking carbon offset projects with the destination country, the tourist is able to get an impression of the co-financed project. In case such projects are realized in cooperation with the hotel, the hotel operator obtains a new tourist attraction and can demonstrate its efforts to climate protection in a PR-effective way.
In automotive a lot of electromagnetically, pyrotechnically or mechanically driven actuators are integrated to run comfort systems and to control safety systems in modern passenger cars. Using shape memory alloys (SMA) the existing systems could be simplified, performing the same function through new mechanisms with reduced size, weight, and costs. A drawback for the use of SMA in safety systems is the lack of materials knowledge concerning the durability of the switching function (long-time stability of the shape memory effect). Pedestrian safety systems play a significant role to reduce injuries and fatal casualties caused by accidents. One automotive safety system for pedestrian protection is the bonnet lifting system. Based on such an application, this article gives an introduction to existing bonnet lifting systems for pedestrian protection, describes the use of quick changing shape memory actuators and the results of the study concerning the long-time stability of the tested NiTi-wires. These wires were trained, exposed up to 4years at elevated temperatures (up to 140°C) and tested regarding their phase change temperatures, times, and strokes. For example, it was found that A P-temperature is shifted toward higher temperatures with longer exposing periods and higher temperatures. However, in the functional testing plant a delay in the switching time could not be detected. This article gives some answers concerning the long-time stability of NiTi-wires that were missing till now. With this knowledge, the number of future automotive applications using SMA can be increased. It can be concluded, that the use of quick changing shape memory actuators in safety systems could simplify the mechanism, reduce maintenance and manufacturing costs and should be insertable also for other automotive applications.
Algorithms for calculating the string edit distance are used in e.g. information retrieval and document analysis systems or for evaluation of text recognizers. Text recognition based on CTC-trained LSTM networks includes a decoding step to produce a string, possibly using a language model, and evaluation using the string edit distance. The decoded string can further be used as a query for database search, e.g. in document retrieval. We propose to closely integrate dictionary search with text recognition to train both combined in a continuous fashion. This work shows that LSTM networks are capable of calculating the string edit distance while allowing for an exchangeable dictionary to separate learned algorithm from data. This could be a step towards integrating text recognition and dictionary search in one deep network.
We have analyzed a pool of 37,839 articles published in 4,404 business-related journals in the entrepreneurship research field using a novel literature review approach that is based on machine learning and text data mining. Most papers have been published in the journals ‘Small Business Economics’, ‘International Journal of Entrepreneurship and Small Business’, and ‘Sustainability’ (Switzerland), while the sum of citations is highest in the ‘Journal of Business Venturing’, ‘Entrepreneurship Theory and Practice’, and ‘Small Business Economics’. We derived 29 overarching themes based on 52 identified clusters. The social entrepreneurship, development, innovation, capital, and economy clusters represent the largest ones among those with high thematic clarity. The most discussed clusters measured by the average number of citations per assigned paper are research, orientation, capital, gender, and growth. Clusters with the highest average growth in publications per year are social entrepreneurship, innovation, development, entrepreneurship education, and (business-) models. Measured by the average yearly citation rate per paper, the thematic cluster ‘research’, mostly containing literature studies, received most attention. The MLR allows for an inclusion of a significantly higher number of publications compared to traditional reviews thus providing a comprehensive, descriptive overview of the whole research field.
The effect on the mean-variance space of restrictions on a variable is investigated in this
paper. A restriction may be the placing of upper and lower bounds on a variable.
Another limitation is the loss of the continuity of a variable.
Average marks for Examinations are considered in an application of this limited meanvariance
space. In this case, the bounds are given by the highest and the lowest possible mark (e.g. 1.0 and 5.0). The limitation of the mean-variance space depends on the number of students who participate in the examination. The restriction of the loss of continuity is shown by the use of discrete marks (e.g. 1.0, 1.3, 1.7, 2.0, …). Furthermore,
the Target-Shortfall-Probability lines are integrated into the mean-variance space. These
lines are used to indicate the proportion of students who have good or very good marks in the examination. In financial markets, Target-Shortfall-Probability is used as a risk criterion.
Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject’s sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system’s performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects.
Modular arithmetic over integers is required for many cryptography systems. Montgomeryreduction is an efficient algorithm for the modulo reduction after a multiplication. Typically, Mont-gomery reduction is used for rings of ordinary integers. In contrast, we investigate the modularreduction over rings of Gaussian integers. Gaussian integers are complex numbers where the real andimaginary parts are integers. Rings over Gaussian integers are isomorphic to ordinary integer rings.In this work, we show that Montgomery reduction can be applied to Gaussian integer rings. Twoalgorithms for the precision reduction are presented. We demonstrate that the proposed Montgomeryreduction enables an efficient Gaussian integer arithmetic that is suitable for elliptic curve cryptogra-phy. In particular, we consider the elliptic curve point multiplication according to the randomizedinitial point method which is protected against side-channel attacks. The implementation of thisprotected point multiplication is significantly faster than comparable algorithms over ordinary primefields.
Offline handwriting recognition systems often use LSTM networks, trained with line- or word-images. Multi-line text makes it necessary to use segmentation to explicitly obtain these images. Skewed, curved, overlapping, incorrectly written text, or noise can lead to errors during segmentation of multi-line text and reduces the overall recognition capacity of the system. Last year has seen the introduction of deep learning methods capable of segmentation-free recognition of whole paragraphs. Our method uses Conditional Random Fields to represent text and align it with the network output to calculate a loss function for training. Experiments are promising and show that the technique is capable of training a LSTM multi-line text recognition system.
Mutual Information Analysis for Generalized Spatial Modulation Systems With Multilevel Coding
(2022)
Generalized Spatial Modulation (GSM) enables a trade-off between very high spectral efficiencies and low hardware costs for massive MIMO systems. This is achieved by transmitting information via the selection of active antennas from a set of available antennas besides the transmission of conventional data symbols. GSM systems have been investigated concerning various aspects like suitable signal constellations, efficient detection algorithms, hardware implementations, spatial precoding, and error control coding. On the other hand, determining the capacity of GSM is challenging because no closed-form expressions have been found so far. This paper investigates the mutual information for different GSM variants. We consider a multilevel coding approach, where the antenna selection and IQ modulation are encoded independently. Combined with multistage decoding, such an approach enables low-complexity capacity-achieving coded modulation. The influence of the data symbols on the mutual information is illuminated. We analyze the portions of mutual information related to antenna selection and the IQ modulation processes which depend on the GSM variant and the signal constellation. Moreover, the potential of spatial modulation for massive MIMO systems with many transmit antennas is investigated. Especially in systems with many transmit antennas much information can be conveyed by antenna selection.
This diploma thesis is devoted to the design and analysis of a radar signal enabling an object classification capability in surveillance radar systems based on high-resolution radar range profiles. It picks up the research results from Kastinger (2006), who investigated classification algorithms for high-resolution radar range profiles, and Meier (2007), who programmed a MATLAB toolbox for the evaluation of radar signals. A classical, brief, introduction to radar fundamentals is given (Chapter 1) as well as the motivation for this thesis and certain basic parameters used. After high-resolution radar range profiles are discussed with special focus on surveillance radar systems (Chapter 2), the results of Kastinger (2006) are picked up (Chapter 3) as far as necessary for the following chapters of this thesis. Following the chapters on radar basics, high-resolution radar range profiles and classification, basic and advanced radar signals are discussed and analysed, especially their range resolution and sidelobe levels (Chapter 4). This includes linear frequency-modulated pulses and nonlinear frequency-modulated pulses as well as phase-coded pulses, coherent trains of identical pulses, and stepped-frequency waveforms. Their analysis is based on Meier's MATLAB toolbox. In Chapter 5 we will bring up additional points that have to be considered in radar system design for implementing a classification capability, before this thesis ends with an overall conclusion (Chapter 6).
Background: Polysomnography (PSG) is the gold standard for detecting obstructive sleep apnea (OSA). However, this technique has many disadvantages when using it outside the hospital or for daily use. Portable monitors (PMs) aim to streamline the OSA detection process through deep learning (DL).
Materials and methods: We studied how to detect OSA events and calculate the apnea-hypopnea index (AHI) by using deep learning models that aim to be implemented on PMs. Several deep learning models are presented after being trained on polysomnography data from the National Sleep Research Resource (NSRR) repository. The best hyperparameters for the DL architecture are presented. In addition, emphasis is focused on model explainability techniques, concretely on Gradient-weighted Class Activation Mapping (Grad-CAM).
Results: The results for the best DL model are presented and analyzed. The interpretability of the DL model is also analyzed by studying the regions of the signals that are most relevant for the model to make the decision. The model that yields the best result is a one-dimensional convolutional neural network (1D-CNN) with 84.3% accuracy.
Conclusion: The use of PMs using machine learning techniques for detecting OSA events still has a long way to go. However, our method for developing explainable DL models demonstrates that PMs appear to be a promising alternative to PSG in the future for the detection of obstructive apnea events and the automatic calculation of AHI.
In 3D extended object tracking (EOT), well-established models exist for tracking the object extent using various shape priors. A single update, however, has to be performed for every measurement using these models leading to a high computational runtime for high-resolution sensors. In this paper, we address this problem by using various model-independent downsampling schemes based on distance heuristics and random sampling as pre-processing before the update. We investigate the methods in a simulated and real-world tracking scenario using two different measurement models with measurements gathered from a LiDAR sensor. We found that there is a huge potential for speeding up 3D EOT by dropping up to 95\% of the measurements in our investigated scenarios when using random sampling. Since random sampling, however, can also result in a subset that does not represent the total set very well, leading to a poor tracking performance, there is still a high demand for further research.
Interpretability and uncertainty modeling are important key factors for medical applications. Moreover, data in medicine are often available as a combination of unstructured data like images and structured predictors like patient’s metadata. While deep learning models are state-of-the-art for image classification, the models are often referred to as ’black-box’, caused by the lack of interpretability. Moreover, DL models are often yielding point predictions and are too confident about the parameter estimation and outcome predictions.
On the other side with statistical regression models, it is possible to obtain interpretable predictor effects and capture parameter and model uncertainty based on the Bayesian approach. In this thesis, a publicly available melanoma dataset, consisting of skin lesions and patient’s age, is used to predict the melanoma types by using a semi-structured model, while interpretable components and model uncertainty is quantified. For Bayesian models, transformation model-based variational inference (TM-VI) method is used to determine the posterior distribution of the parameter. Several model constellations consisting of patient’s age and/or skin lesion were implemented and evaluated. Predictive performance was shown to be best by using a combined model of image and patient’s age, while providing the interpretable posterior distribution of the regression coefficient is possible. In addition, integrating uncertainty in image and tabular parts results in larger variability of the outputs corresponding to high uncertainty of the single model components.
Forecasting is crucial for both system planning and operations in the energy sector. With increasing penetration of renewable energy sources, increasing fluctuations in the power generation need to be taken into account. Probabilistic load forecasting is a young, but emerging research topic focusing on the prediction of future uncertainties. However, the majority of publications so far focus on techniques like quantile regression, ensemble, or scenario-based methods, which generate discrete quantiles or sets of possible load curves. The conditioned probability distribution remains unknown and can only be estimated when the output is post-processed using a statistical method like kernel density estimation.
Instead, the proposed probabilistic deep learning model uses a cascade of transformation functions, known as normalizing flow, to model the conditioned density function from a smart meter dataset containing electricity demand information for over 4,000 buildings in Ireland. Since the whole probability density function is tractable, the parameters of the model can be obtained by minimizing the negative loglikelihood through the state of the art gradient descent. This leads to the model with the best representation of the data distribution.
Two different deep learning models have been compared, a simple three-layer fully connected neural network and a more advanced convolutional neural network for sequential data processing inspired by the WaveNet architecture. These models have been used to parametrize three different probabilistic models, a simple normal distribution, a Gaussian mixture model, and the normalizing flow model. The prediction horizon is set to one day with a resolution of 30 minutes, hence the models predict 48 conditioned probability distributions.
The normalizing flow model outperforms the two other variants for both architectures and proves its ability to capture the complex structures and dependencies causing the variations in the data. Understanding the stochastic nature of the task in such detail makes the methodology applicable for other use cases apart from forecasting. It is shown how it can be used to detect anomalies in the power grid or generate synthetic scenarios for grid planning.
Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows
(2021)
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level. However, high fluctuations and increasing electrification cause huge forecast errors with traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus enables various applications in low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein-Polynomial Normalizing Flows where a neural network controls the parameters of the flow. In an empirical study with 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities and also outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.
The target of this thesis is the introduction of a client management system (CMS) at Haaland Internet Productions (HiP), a web design and hosting company in Burbank, California, USA. The company needs a system to track orders and improve workflow. HiP needs a system which not only tracks orders, but also stores all client information in a database. This client information can be used for a variety of marketing and contact reasons. It is an important and integral part of HiP's client relationship management (CRM). The lack of a cohesive CMS at HiP caused many fundamental business problems, such as lost orders, missed billing statements, and over/under billing. The research done during the investigation and analysis of the company and their needs should lead to a global system which totally fulfils the needs of HiP. This global system could be in the form of an off-the-shelf product with some customizations, or a completely new, in-house system. Either solution will have respective pros and cons; the goal is to reach a decision that best fits HiP's needs and situation. The following is a concise version of the project. Particular emphasis is placed upon the single steps which made up the decision process, as well as the practiced techniques, methods, and their applications.
In tourism, energy demands are particularly high.Tourism facilities such as hotels require large amounts ofelectric and heating resp. cooling energy. Their supply howeveris usually still based on fossil energies. This research approachanalyses the potential of promoting renewable energies in BlackForest tourism. It focuses on a combined and hence highlyefficient production of both electric and thermal energy bybiogas plants on the one hand and its provision to local tourismfacilities via short distance networks on the other. Basing onsurveys and qualitative empiricism and considering regionalresource availability as well as socio-economic aspects, it thusexamines strengths, weaknesses, opportunities and threats thatcan arise from such a cooperation.
In tourism, energy demands are particularly high. Tourism facilities such as hotels require large amounts of electric and heating / cooling energy while their supply is usually still based on fossil energies.
This research approach analyses the potential of promoting renewable energies in tourism. It focuses on a combined and hence highly efficient production of both electric and thermal energy by biogas plants on the one hand and its provision to local tourism facilities via short distance networks on the other. Considering regional resource availability as well as socio-economic aspects, it thus examines strengths, weaknesses, opportunities and threats that can arise from such a micro-cooperation. The research aim is to provide an actor-based, spatially transferable feasibility analysis.
The performance and reliability of non-volatile NAND flash memories deteriorate as the number of program/erase cycles grows. The reliability also suffers from cell to cell interference, long data retention time, and read disturb. These processes effect the read threshold voltages. The aging of the cells causes voltage shifts which lead to high bit error rates (BER) with fixed pre-defined read thresholds. This work proposes two methods that aim on minimizing the BER by adjusting the read thresholds. Both methods utilize the number of errors detected in the codeword of an error correction code. It is demonstrated that the observed number of errors is a good measure for the voltage shifts and is utilized for the initial calibration of the read thresholds. The second approach is a gradual channel estimation method that utilizes the asymmetrical error probabilities for the one-to-zero and zero-to-one errors that are caused by threshold calibration errors. Both methods are investigated utilizing the mutual information between the optimal read voltage and the measured error values.
Numerical results obtained from flash measurements show that these methods reduce the BER of NAND flash memories significantly.