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Ballistocardiography (BCG) can be used to monitor heart rate activity. Besides, the accelerometer should have high sensitivity and minimal internal noise; a low-cost approach was taken into consideration. Several measurements have been executed to determine the optimal positioning of a sensor under the mattress to obtain a signal strong enough for further analysis. A prototype for an unobtrusive accelerometer-based measurement system has been developed and tested in a conventional bed without any specific extras. The influence of the human sleep position for the output accelerometer data was tested. The obtained results indicate the potential to capture BCG signals using accelerometers. The measurement system can detect heart rate in an unobtrusive form in the home environment.
We compared vulnerable and fixed versions of the source code of 50 different PHP open source projects based on CVE reports for SQL injection vulnerabilities. We scanned the source code with commercial and open source tools for static code analysis. Our results show that five current state-of-the-art tools have issues correctly marking vulnerable and safe code. We identify 25 code patterns that are not detected as a vulnerability by at least one of the tools and 6 code patterns that are mistakenly reported as a vulnerability that cannot be confirmed by manual code inspection. Knowledge of the patterns could help vendors of static code analysis tools, and software developers could be instructed to avoid patterns that confuse automated tools.
The last decades have shown that the volume of tourism, in general, is constantly increasing (with some justified exceptions). To offer a possibility of travel for all groups of people, it is necessary to pay attention to accessibility. One of the possibilities for increasing accessibility is digital technologies, which could assist in planning and the implementation and completion of trips. To make a selection of technologies, first, a study of barriers was conducted, which was then analyzed, and finally, some technologies were made available in a test setup. A focus on two technologies was made: 360°-Tours and mobile app with the travel information. The two technologies were implemented and presented to the test subjects.
The evaluation results showed that both technologies could increase accessibility if some essential aspects (such as usability, completeness, relevance, etc.) are considered during the implementation.
The development of home health systems can provide continuous and user-friendly monitoring of key health parameters. This project aims to create a concept for such a system, implement it on a test basis, and evaluate it. Three health areas were selected for this purpose:
Sleep, Stress, and Rehabilitation. Appropriate devices were installed in the homes of test subjects and used by them for two weeks. Besides, relevant questionnaires were completed to obtain a complete picture. Finally, the implemented system was evaluated, and the results of the conducted study showed that home health systems have great potential. However, it is necessary to consider some points to increase the usability of the system and the motivation of the users. Among others, ease of use of the equipment is of extreme importance.
Health monitoring in a home environment can have broader use since it may provide continuous control of health parameters with relatively minor intrusiveness into regular life. This work aims to verify if it is possible to replace the typical in some sleep medicine areas subjective questioning by an objective measurement using electronic devices. For this purpose, a study was conducted with ten subjects, in which objective and subjective measurement of relevant sleep parameters took place. The results of both measurement methods were evaluated and analyzed. The results showed that while for some measures, such as Total Time in Bed, there is a high agreement between objective and subjective measurements, for others, such as sleep quality, there are significant differences. For this reason, currently, a combination of both measurement methods may be beneficial and provide the most detailed results, while a partial replacement can already reduce the number of questions at the subjective measurement by measurement through electronic devices.
Identifikation von Schlaf- und Wachzuständen durch die Auswertung von Atem- und Bewegungssignalen
(2021)
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 digitalised, plagued by delays, cost overruns and benefit shortfalls (Cantarelli et al. 2008; Flyvbjerg, 2007; Flyvbjerg et al., 2003; Flyvbjerg et al., 2004). The root cause is the prevailing fragmentation of the infrastructure sector (Fellows and Liu, 2012). To help overcome these challenges, integration of the value chain is needed. This could be achieved through a use-case-based creation of federated ecosystems connecting open and trusted data spaces and advanced services applied to infrastructure projects. Such digital platforms enable full-lifecycle participation and responsible governance guided by a shared infrastructure vision. Digital federation enables secure and sovereign data exchange and thus collaboration across the silos within the infrastructure sector and between industries as well as within and between countries. Such an approach to infrastructure technology policy would not rely on technological solutionism but proposes the development of open and trusted data alliances. Federated data spaces provide access to the emerging data economy, especially for SMEs, and can foster the innovation of new digital services. Such responsible digital governance can help make the infrastructure sector more resilient, efficient and aligned with the realisation of ambitious decarbonisation and environmental protection targets. The European Union and the United States have already developed architectures for sovereign and secure data exchange.
Preliminary results of homomorphic deconvolution application to surface EMG signals during walking
(2021)
Homomorphic deconvolution is applied to sEMG signals recorded during walking. Gastrocnemius lateralis and tibialis anterior signals were acquired according to SENIAM recommendation. MUAP parameters like amplitude and scale were estimated, whilst the MUAP shape parameter was fixed. This features a useful time-frequency representation of sEMG signal. Estimation of scale MUAP parameter was verified extracting the mean frequency of filtered EMG signal, extracted from the scale parameter estimated with two different MUAP shape values.
Normal breathing during sleep is essential for people’s health and well-being. Therefore, it is crucial to diagnose apnoea events at an early stage and apply appropriate therapy. Detection of sleep apnoea is a central goal of the system design described in this article. To develop a correctly functioning system, it is first necessary to define the requirements outlined in this manuscript clearly. Furthermore, the selection of appropriate technology for the measurement of respiration is of great importance. Therefore, after performing initial literature research, we have analysed in detail three different methods and made a selection of a proper one according to determined requirements. After considering all the advantages and disadvantages of the three approaches, we decided to use the impedance measurement-based one. As a next step, an initial conceptual design of the algorithm for detecting apnoea events was created. As a result, we developed an activity diagram on which the main system components and data flows are visually represented.
Respiratory diseases are leading causes of death and disability in the world. The recent COVID-19 pandemic is also affecting the respiratory system. Detecting and diagnosing respiratory diseases requires both medical professionals and the clinical environment. Most of the techniques used up to date were also invasive or expensive.
Some research groups are developing hardware devices and techniques to make possible a non-invasive or even remote respiratory sound acquisition. These sounds are then processed and analysed for clinical, scientific, or educational purposes.
We present the literature review of non-invasive sound acquisition devices and techniques.
The results are about a huge number of digital tools, like microphones, wearables, or Internet of Thing devices, that can be used in this scope.
Some interesting applications have been found. Some devices make easier the sound acquisition in a clinic environment, but others make possible daily monitoring outside that ambient. We aim to use some of these devices and include the non-invasive recorded respiratory sounds in a Digital Twin system for personalized health.
Innovation Labs
(2021)
Today's increasing pace of change and intense competition places demands on organizations to use a different approach to innovation, going beyond the incremental innovation that is typically developed within the core of the organization. As an option to escape the existing beliefs of the core organization, innovation labs are used to develop more discontinuous innovation. Despite the abundance of these so-called innovation labs in practice, researchers have devoted little effort to scrutinizing the concept and to provide managers with a framework for exploiting this form of innovation. In this paper, we aim to perform an empirical investigation and to create a consensus around the concept of innovation labs. To do so, we conducted a multiple case study in large international organizations with a total of 31 interviews of an average length of 70 minutes. We offer a framework by identifying four innovation lab types and consider when each is most appropriate. Furthermore, we highlight the importance for managers and their organizations to align the strategic intent with the innovation lab type as well as the interface between the innovation lab and the core business.
Text produced by entrepreneurs represents a data source in entrepreneurship research on venture performance and fund-raising success. Manual text coding of single variables is increasingly assisted or replaced by computer-aided text analysis. Yet, for the development of prediction models with several variables, such dictionary-based text analysis methods are less suitable. Natural language processing techniques are an alternative; however, the implementation is more complex and requires substantial programming skills. More work is required to understand how text analytics can advance entrepreneurship research. This study hence experiments with different artificial intelligence methods rooted in Natural Language Processing and deep learning. It uses 766 business plans to train a model for the automated measurement of transaction relations, a construct which is an indicator for new technology-based firm survival. Empirical findings show that the accuracy of construct measurement can be significantly increased with automated methods and improves with larger amounts of training data. Language complexity sets limits to the precision of automated construct measurement though. We therefore recommend a hybrid approach: making use of the inherent advantages of combining automated with human coding until the amount of training data is sufficiently large to substitute the human coding completely. The study provides insights into the applicability of different text analytics methods in entrepreneurship research and points at future research potential.
Cultural Mapping 4.0
(2021)
Die Bodenseeregion gehört zu einer der ältesten Kulturlandschaften Europas. Ihre regionale kulturelle Identität trägt zum Image sowie Identifikation seitens der Bevölkerung mit der Bodenseeregion bei. Dennoch mangelt es an einer ganzheitlichen, die gesamte Bodenseeregion umfassende Betrachtung der Frage, was die kulturelle Identität der Bodenseeregion ausmacht. Das Forschungsprojekt «CultMap4.0» hat daher zum Ziel, aus einer räumlichen Perspektive die Wechselwirkung zwischen regionaler Identität, Kultur und Mobilität zu untersuchen. Neben der Leitfrage, was kulturelle Identität in einer grenzüberschreitenden und diversen Region wie dem Bodensee zu sein und leisten vermag, werden im Rahmen von vier Themenschwerpunkten folgende Forschungsfragen untersucht: Wie nehmen einheimische Bevölkerung, Unternehmen und TouristInnen die regionale kulturelle Identität (Eigenbild) und das Image (Fremdbild) der Bodenseeregion wahr? Wie kann der Ansatz des “Cultural Mappings” durch partizipative Kartierung digital transformiert werden, und wie können kulturelle Identität und Mobilität mit digitalem Storytelling in Storymaps visualisiert werden? Welchen Beitrag kann “Cultural Mapping 4.0” als partizipatives Werkzeug zur Regionalplanung und zur Kommunikation mit Stakeholdern in der Bodenseeregion und anderenorts leisten? Die dabei entstehenden Storymaps – interaktive Webinhalte aus Texten, Karten und weiteren Medien – zur kulturellen Identität der Bodenseeregion sollen auf der Plattform “Cultural Mapping” Project Lake Constance” veröffentlicht werden, um so von den Stakeholdern als Planungs- und Entscheidungstool sowie fürs Standortmarketing genutzt werden zu können.
Acoustic Echo Cancellation (AEC) plays a crucial role in speech communication devices to enable full-duplex communication. AEC algorithms have been studied extensively in the literature. However, device specific details like microphone or loudspeaker configurations are often neglected, despite their impact on the echo attenuation or near-end speech quality. In this work, we propose a method to investigate different loudspeaker-microphone configurations with respect to their contribution to the overall AEC performance. A generic AEC system consisting of an adaptive filter and a Wiener post filter is used for a fair comparison between different setups. We propose the near-end-to-residual-echo ratio (NRER) and the attenuation-of-near-end (AON) as quality measures for the full-duplex AEC performance.
Female Entrepreneurship has gained interest over the last 20 years. Therefore, this paper analyses 7,320 articles of the research field ‘women in entrepreneurial context’ published in 885 journals. The sample is analyzed by using a machine learning and text mining based methodological approach. Aiming to provide a broad overview over the research literature, 41 clusters and 11 superordinate topics were identified. Major developments of research attention are outlined by analyzing bibliometric data of the period from 2000 to 2020. Overall growth in terms of research attention measured by the development of yearly citations per article is best noticeable in clusters ‘corporate social responsibility’, ‘brand’, and ‘corporate (-governance)’, and in superordinate topics ‘performance’, ‘education’, and ‘corporate (board/ management)’. There are also indicators for an overall increase of research attention and cluster variety. The synthesis provides an insight into most trending superordinate topics. Therefore, this literature review gives a comprehensive and descriptive overview as well as an insight into thematic trend developments of the research field.
The encoding of antenna patterns with generalized spatial modulation as well as other index modulation techniques require w-out-of-n encoding where all binary vectors of length n have the same weight w. This constant-weight property cannot be obtained by conventional linear coding schemes. In this work, we propose a new class of constant-weight codes that result from the concatenation of convolutional codes with constant-weight block codes. These constant-weight convolutional codes are nonlinear binary trellis codes that can be decoded with the Viterbi algorithm. Some constructed constant-weight convolutional codes are optimum free distance codes. Simulation results demonstrate that the decoding performance with Viterbi decoding is close to the performance of the best-known linear codes. Similarly, simulation results for spatial modulation with a simple on-off keying show a significant coding gain with the proposed coded index modulation scheme.
List decoding for concatenated codes based on the Plotkin construction with BCH component codes
(2021)
Reed-Muller codes are a popular code family based on the Plotkin construction. Recently, these codes have regained some interest due to their close relation to polar codes and their low-complexity decoding. We consider a similar code family, i.e., the Plotkin concatenation with binary BCH component codes. This construction is more flexible regarding the attainable code parameters. In this work, we consider a list-based decoding algorithm for the Plotkin concatenation with BCH component codes. The proposed list decoding leads to a significant coding gain with only a small increase in computational complexity. Simulation results demonstrate that the Plotkin concatenation with the proposed decoding achieves near maximum likelihood decoding performance. This coding scheme can outperform polar codes for moderate code lengths.
Nowadays, the inexpensive memory space promotes an accelerating growth of stored image data. To exploit the data using supervised Machine or Deep Learning, it needs to be labeled. Manually labeling the vast amount of data is time-consuming and expensive, especially if human experts with specific domain knowledge are indispensable. Active learning addresses this shortcoming by querying the user the labels of the most informative images first. One way to obtain the ‘informativeness’ is by using uncertainty sampling as a query strategy, where the system queries those images it is most uncertain about how to classify. In this paper, we present a web-based active learning framework that helps to accelerate the labeling process. After manually labeling some images, the user gets recommendations of further candidates that could potentially be labeled equally (bulk image folder shift). We aim to explore the most efficient ‘uncertainty’ measure to improve the quality of the recommendations such that all images are sorted with a minimum number of user interactions (clicks). We conducted experiments using a manually labeled reference dataset to evaluate different combinations of classifiers and uncertainty measures. The results clearly show the effectiveness of an uncertainty sampling with bulk image shift recommendations (our novel method), which can reduce the number of required clicks to only around 20% compared to manual labeling.
Summary of the 9th workshop on metallization and interconnection for crystalline silicon solar cells
(2021)
The 9th edition of the Workshop on Metallization and Interconnection for Crystalline Silicon Solar Cells was held as an online event but nevertheless reached the workshop goals of knowledge sharing and networking. The technology of screen-printed contacts of high temperature pastes continues its fast progress enabled by better understanding of the phenomena taking place during printing and firing, and progress in materials. Great improvements were also achieved in low temperature paste printing and plated metallization. In the field of interconnection, progress was reported on multiwire approaches, electrically conductive adhesives and on foil-based approaches. Common to many contributions at the workshop was the use of advanced laser processes to improve performance or throughput.
Continuous range queries are a common means to handle mobile clients in high-density areas. Most existing approaches focus on settings in which the range queries for location-based services are mostly static whereas the mobile clients in the ranges move. We focus on a category called Dynamic Real-Time Range Queries (DRRQ) assuming that both, clients requested by the query and the inquirers, are mobile. In consequence, the query parameters results continuously change. This leads to two requirements: the ability to deal with an arbitrary high number of mobile nodes (scalability) and the real-time delivery of range query results. In this paper we present the highly decentralized solution Adaptive Quad Streaming (AQS) for the requirements of DRRQs. AQS approximates the query results in favor of a controlled real-time delivery and guaranteed scalability. While prior works commonly optimizes data structures on servers, we use AQS to focus on a highly distributed cell structure without data structures automatically adapting to changing client distributions. Instead of the commonly used request-response approach, we apply a lightweight streaming method in which no bidirectional communication and no storage or maintenance of queries are required at all.
Trajectory Tracking of a Fully-actuated Surface Vessel using Nonlinear Model Predictive Control
(2021)
The trajectory tracking problem for a fully-actuated real-scaled surface vessel is addressed in this paper. The unknown hydrodynamic and propulsion parameters of the vessel’s dynamic model were identified using an experimental maneuver-based identification process. Then, a nonlinear model predictive control (NMPC) scheme is designed and the controller’s performance is assessed through the variation of NMPC parameters and constraints tightening for tracking a curved trajectory.
This paper describes the development of a control system for an industrial heating application. In this process a moving substrate is passing through a heating zone with variable speed. Heat is applied by hot air to the substrate with the air flow rate being the manipulated variable. The aim is to control the substrate’s temperature at a specific location after passing the heating zone. First, a model is derived for a point attached to the moving substrate. This is modified to reflect the temperature of the moving substrate at the specified location. In order to regulate the temperature a nonlinear model predictive control approach is applied using an implicit Euler scheme to integrate the model and an augmented gradient based optimization approach. The performance of the controller has been validated both by simulations and experiments on the physical plant. The respective results are presented in this paper.
In multi-extended object tracking, parameters (e.g., extent) and trajectory are often determined independently. In this paper, we propose a joint parameter and trajectory (JPT) state and its integration into the Bayesian framework. This allows processing measurements that contain information about parameters and states. Examples of such measurements are bounding boxes given from an image processing algorithm. It is shown that this approach can consider correlations between states and parameters. In this paper, we present the JPT Bernoulli filter. Since parameters and state elements are considered in the weighting of the measurement data assignment hypotheses, the performance is higher than with the conventional Bernoulli filter. The JPT approach can be also used for other Bayes filters.
The main aim of presented in this manuscript research is to compare the results of objective and subjective measurement of sleep quality for older adults (65+) in the home environment. A total amount of 73 nights was evaluated in this study. Placing under the mattress device was used to obtain objective measurement data, and a common question on perceived sleep quality was asked to collect the subjective sleep quality level. The achieved results confirm the correlation between objective and subjective measurement of sleep quality with the average standard deviation equal to 2 of 10 possible quality points.
Cultural Mapping 4.0
(2021)
Cultural mapping aims to capture and visualize tangible and intangible cultural assets. This extend abstract proposes the consequent extension of analogue forms of cultural mapping using digital technologies, and its contribution is two-fold. First, the necessary theoretical basis is provided by a literature review of the still-young field of cultural mapping and the complementary disciplines of participatory mapping and digital story-mapping. Second, we propose a digitally enhanced Cultural Mapping 4.0 vision based on a case study from an ongoing research project in the Lake Constance region. Digital participatory mapping approaches are applied to capture data, and to validate and disseminate the results, story-mapping - a spatial form of digital storytelling - is used.
This paper presents a generic method to enhance performance and incorporate temporal information for cardiorespiratory-based sleep stage classification with a limited feature set and limited data. The classification algorithm relies on random forests and a feature set extracted from long-time home monitoring for sleep analysis. Employing temporal feature stacking, the system could be significantly improved in terms of Cohen’s κ and accuracy. The detection performance could be improved for three classes of sleep stages (Wake, REM, Non-REM sleep), four classes (Wake, Non-REM-Light sleep, Non-REM Deep sleep, REM sleep), and five classes (Wake, N1, N2, N3/4, REM sleep) from a κ of 0.44 to 0.58, 0.33 to 0.51, and 0.28 to 0.44 respectively by stacking features before and after the epoch to be classified. Further analysis was done for the optimal length and combination method for this stacking approach. Overall, three methods and a variable duration between 30 s and 30 min have been analyzed. Overnight recordings of 36 healthy subjects from the Interdisciplinary Center for Sleep Medicine at Charité-Universitätsmedizin Berlin and Leave-One-Out-Cross-Validation on a patient-level have been used to validate the method.
Since its first edition in 2008, the Workshop on Metallization and Interconnection for Crystalline Silicon SolarCells has been a key event where knowledge in the critical fields of crystalline silicon solar cell metallization andinterconnection is shared between experts from academia and industry. It has become a highly recognized event forthe quality of the contributions, the lively Q&A sessions, and the exceptional networking opportunity.The situation with the Covid-19 pandemic made organizing the 9th edition as an in-person event impossible andforced us to reconsider the event format. The event took place virtually on October 5th and 6th 2020. We used aninnovative online platform that enabled not only presentations followed by Q&A but also more informal interactions,where participants could see and talk directly to other participants. 120 experts from 22 countries took part andattended 21 contributions presented live. In spite of a few technical glitches, the workshop was successful and thegoals of exchanging on the state-of-the-art in research/industry and connecting experts in the field were achieved.All presentations are available on www.miworkshop.info as .pdf documents. These proceedings contain asummary of the 9th edition (MIW2020) and peer-reviewed papers based on the workshop contributions. The organizerswish to thank the members of the Scientific Committee for the time spent reviewing the MIW2020 abstracts andproceedings. The organizers also wish to thank again the sponsors and supporters for their financial contributionswhich made the 9th Workshop on Metallization and Interconnection for Crystalline Silicon Solar Cells possible.
In this paper, a systematic comparison of three different advanced control strategies for automated docking of a vessel is presented. The controllers are automatically tuned offline by applying an optimization process using simulations of the whole system including trajectory planner and state and disturbance observer. Then investigations are conducted subject to performance and robustness using Monte Carlos simulation with varying model parameters and disturbances. The control strategies have also been tested in full scale experiments using the solar research vessel Solgenia. The investigated control strategies all have demonstrated very good performance in both, simulation and real world experiments. Videos are available under https://www.htwg-konstanz.de/forschung-und-transfer/institute-und-labore/isd/regelungstechnik/videos/
Guiding through the Fog
(2021)
Corporate Entrepreneurship (CE) programs are formalized efforts to realize entrepreneurial activities in established companies. Despite the growing and evolving landscape of CE programs, effectively managing them remains a challenging endeavor which results in disappointing outcomes and oftentimes leads to the early termination of such programs. We unmask the differences in goal setting of CE programs and highlight that setting appropriate goals is imperative for their desired outcomes. In practice, companies seem to struggle with the goal setting, and scholars have not yet fully solved the puzzle of goals setting in the context of CE programs either. Therefore, we set out to explore the current state of goal setting in the context of CE programs building upon 61 semi-structured interviews with CE program executives from cross-industry companies with different sizes. Our study contributes to a better understanding of goal setting in the context of CE programs by (1) characterizing the goal setting of CE programs based on goal attributes and goal types and (2) identifying differences among the goal setting of CE programs. We provide implications to practice for a more effective management of CE programs and conclude with a discussion for future research on the impact of the different goal settings.
Market research institutes forecast a growing relevance of Low-Code Development Platforms (LCDPs) for organizations. Moreover, the rising number of scientific publications in recent years shows the increasing interest of the academic community. However, an overview of current research focuses and fruitful future research topics is missing. This paper conducts a first scientific literature review on LCDPs to close this gap. The socio-technical system (STS) model, which categorizes information systems into a social and a technical system, serves to analyze the identified 32 publications. Most of current research focuses on the technical system (technology or task). In contrast, only three publications explicitly target the social system (structure or people). Hence, this paper enables future research to address the identified research gaps. Additionally, practitioners gain awareness of technical and social aspects involved in the development, implementation, and application of LCDPs.
Deep transformation models
(2021)
We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks it is predominantly used to just predict a single number. This ignores the non-deterministic character of most tasks. Especially if crucial decisions are based on the predictions, like in medical applications, it is essential to quantify the prediction uncertainty. The presented deep learning transformation model estimates the whole conditional probability distribution, which is the most thorough way to capture uncertainty about the outcome. We combine ideas from a statistical transformation model (most likely transformation) with recent transformation models from deep learning (normalizing flows) to predict complex outcome distributions. The core of the method is a parameterized transformation function which can be trained with the usual maximum likelihood framework using gradient descent. The method can be combined with existing deep learning architectures. For small machine learning benchmark datasets, we report state of the art performance for most dataset and partly even outperform it. Our method works for complex input data, which we demonstrate by employing a CNN architecture on image data.