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In spite of the amount of new tools and methodologies adopted in the road infrastructure sector, the performance of road infrastructure projects is not constantly improving. Considering that the volume of projects undertaken is forecasted to increase every year, this is a substantial issue for the road infrastructure sector. Hence this work focuses on the principles of Blockchain Technology, road infrastructure sector and the information exchange with the aim to use the advantages of the Blockchain Technology in supporting to overcome the various challenges along the life cycle of road infrastructure projects.
Within the scope of this paper, two studies were conducted. First, focus groups were used to explore where society (road infrastructure sector) stands in terms of industry 4.0 and to get a better understanding if and where the principles of Blockchain Technology can be used when managing projects in the road infrastructure sector. Second, semi-structured interviews were administrated with experts of the road infrastructure sector and experts of Blockchain Technology to better understand the interrelation between these two areas. Based on the outcome of the two studies, technology barriers and enablers were explored for the purpose of improved information exchange within the road infrastructure sector.
The two studies revealed that there are significant and strong interrelations between the principles of the Blockchain Technology, project management within the road infrastructure sector and information exchange. These interrelations are complex and diverse, but overall it can be concluded that the adoption of the principles of Blockchain Technology into the field of information exchange improves the management of road infrastructure projects. Based on the two studies a theoretical framework was developed.
In summary this research showed that trust is an important factor and builds the foundation for communication and to ensure a proper information exchange. Within the scope of this thesis, it was demonstrated that the principles of the Blockchain Technology can be used to increase transparency, traceability and immutability during the life cycle of road infrastructure projects in the area of information exchange.
Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.
Motion estimation is an essential element for autonomous vessels. It is used e.g. for lidar motion compensation as well as mapping and detection tasks in a maritime environment. Because the use of gyroscopes is not reliable and a high performance inertial measurement unit is quite expensive, we present an approach for visual pitch and roll estimation that utilizes a convolutional neural network for water segmentation, a stereo system for reconstruction and simple geometry to estimate pitch and roll. The algorithm is validated on a novel, publicly available dataset recorded at Lake Constance. Our experiments show that the pitch and roll estimator provides accurate results in comparison to an Xsens IMU sensor. We can further improve the pitch and roll estimation by sensor fusion with a gyroscope. The algorithm is available in its implementation as a ROS node.
Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition
(2018)
Deep learning is a field of machine learning that has been the focus of active research and successful applications in recent years. Offline handwriting recognition is one of the research fields and applications were deep neural networks have shown high accuracy. Deep learning models and their training pipeline show a large amount of hyper-parameters in their data selection, transformation, network topology and training process that are sometimes interdependent. This increases the overall difficulty and time necessary for building and training a model for a specific data set and task at hand. This work proposes a novel visualization-assisted workflow that guides the model developer through the hyper-parameter search in order to identify relevant parameters and modify them in a meaningful way. This decreases the overall time necessary for building and training a model. The contributions of this work are a workflow for hyper-parameter search in offline handwriting recognition and a heat map based visualization technique for deep neural networks in multi-line offline handwriting recognition. This work applies to offline handwriting recognition, but the general workflow can possibly be adapted to other tasks as well.
Volterra and Wiener series
(2011)
Volterra and Wiener series are two classes of polynomial representations of nonlinear systems. They are perhaps the best understood and most widely used nonlinear system representations in signal processing and system identification. A Volterra or Wiener representation can be thought of as a natural extension of the classical linear system representation. In addition to the convolution of the input signal with the system's impulse response, the system representation includes a series of nonlinear terms that contain products of increasing order of the input signal with itself. It can be shown that these polynomial extension terms allow for representing a large class of nonlinear systems which basically encompasses all systems with scalar outputs that are time-invariant and have noninfinite memory.
Times of high dynamic and growing new knowledge demand for entrepreneurial education and university engagement. Higher education institutions (HEIs) have established intensive knowledge and resources about entrepreneurial education and relating activities and formats over the last years. As smaller companies (SMEs) are increasingly experimenting with entrepreneurship, they seem to struggle with setting up entrepreneurial activities within their established corporate strategy and innovation structures. It is beneficial for them to collaborate with higher education institutions to minimize the risk and uncertainty associated with implementing entrepreneurship education (EE) and catch up with larger corporates. Further, research lacks a systematic characterization of EE activities in those companies and classification of collaboration formats. Therefore, this study uses qualitative research methods to analyze data from interviews conducted with two German SMEs. Our study contributes to a better understanding of EE in SME and respective HEI collaborations by (1) characterizing EE in SME and SME-HEI collaboration based on attributes and collaboration types defined by their locus of collaboration and intensity of knowledge inflow and (2) identifying differences among EE in SME and HEI. We provide implications to practice—corporate and university EE initiatives—for a more effective design and implementation of EE in SMEs and the SME-HEI collaborations themselves.
The cornerstone of cognitive systems is environment awareness which enables agile and adaptive use of channel resources. Whitespace prediction based on learning the statistics of the wireless traffic has proven to be a powerful tool to achieve such awareness. In this paper, we propose a novel Hidden Markov Model (HMM) based spectrum learning and prediction approach which accurately estimates the exact length of the whitespace in WiFi channels within the shared industrial scientific medical ISM) bands. We show that extending the number of hidden states and formulating the prediction problem as a maximum likelihood (ML) classification leads to a substantial increase in the prediction horizon compared to classical approaches that predict the immediate (short-term) future. We verify the proposed algorithm through simulations which utilize a model for WiFi traffic based on extensive measurement campaigns.
Nowadays established companies use Corporate Entrepreneurship (CE) as a means to create discontinuous innovations. Many companies thereby even implement multiple CE units that typically involve several entrepreneurial activities. This explorative study aimed to identify the reasons why established companies implement multiple CE units concurrently. In conducting a comparative case study with eight companies from different industries, valuable insights for science and practice were gained. We provide an overview of different 11 reasons for implementing multiple CE units. This shows that the combination of CE units used by companies differs depending on the reason. It further allowed to derive general approaches of established companies to the implementation of CE units. Last, we identify the concept of co-specialization to be a central driver explaining the creation of the need to set up multiple units. We conclude by indicating implications and subjects for future research.
This work studies a wind noise reduction approach for communication applications in a car environment. An endfire array consisting of two microphones is considered as a substitute for an ordinary cardioid microphone capsule of the same size. Using the decomposition of the multichannel Wiener filter (MWF), a suitable beamformer and a single-channel post filter are derived. Due to the known array geometry and the location of the speech source, assumptions about the signal properties can be made to simplify the MWF beamformer and to estimate the speech and noise power spectral densities required for the post filter. Even for closely spaced microphones, the different signal properties at the microphones can be exploited to achieve a significant reduction of wind noise. The proposed beamformer approach results in an improved speech signal regarding the signal-to-noise-ratio and keeps the linear speech distortion low. The derived post filter shows equal performance compared to known approaches but reduces the effort for noise estimation.