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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.
Despite the increased attention dedicated to research on the antecedents and determinants of new venture survival in entrepreneurship, defining and capturing survival as an outcome represents a challenge in quantitative studies. This paper creates awareness for ventures being inactive while still classified as surviving based on the data available. We describe this as the ‘living dead’ phenomenon, arguing that it yields potential effects on the empirical results of survival studies. Based on a systematic literature review, we find that this issue of inactivity has not been sufficiently considered in previous new venture survival studies. Based on a sample of 501 New Technology-Based Firms, we empirically illustrate that the classification of living dead ventures into either survived or failed can impact the factors determining survival. On this basis, we contribute to an understanding of the issue by defining the ‘living dead’ phenomenon and by proposing recommendations for research practice to solve this issue in survival studies, taking the data source, the period under investigation and the sample size into account.
In today's volatile market environments, companies must be able to continuously innovate. In this context, innovation does not only refer to the development of new products or business models but often also affects the entire organization, which has to transform its structures, processes, and ways of working.Corporate entrepreneurship (CE) programs are often used by established companies to address these innovation and transformation challenges. In general, they are understood as formalized entrepreneurial activities to (1) support internal corporate ventures or (2) work with external startups. The organizational design and value creation of CE programs exhibit a high degree of heterogeneity. On the one hand, this heterogeneity makes CE programs a valuable management tool that can be used for many purposes. On the other hand, it can be seen as a reason for the current challenges that companies experience in effectively using and managing CE programs.By systematically analyzing 54 different cases in established companies in Germany, Switzerland, and Austria, this study contributes to a better understanding of the heterogeneity of CE programs. The taxonomic approach provides clearly defined types of CE programs that are distinguished according to their organizational design and the outputs they generate.
The main challenge in Bayesian models is to determine the posterior for the model parameters. Already, in models with only one or few parameters, the analytical posterior can only be determined in special settings. In Bayesian neural networks, variational inference is widely used to approximate difficult-to-compute posteriors by variational distributions. Usually, Gaussians are used as variational distributions (Gaussian-VI) which limits the quality of the approximation due to their limited flexibility. Transformation models on the other hand are flexible enough to fit any distribution. Here we present transformation model-based variational inference (TM-VI) and demonstrate that it allows to accurately approximate complex posteriors in models with one parameter and also works in a mean-field fashion for multi-parameter models like neural networks.
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.
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.
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.
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.
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.
In this paper, rectangular matrices whose minors of a given order have the same strict sign are considered and sufficient conditions for their recognition are presented. The results are extended to matrices whose minors of a given order have the same sign or are allowed to vanish. A matrix A is called oscillatory if all its minors are nonnegative and there exists a positive integer k such that A^k has all its minors positive. As a generalization, a new type of matrices, called oscillatory of a specific order, is introduced and some of their properties are investigated.
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.
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.
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.
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.
In this paper, a novel measurement model based on spherical double Fourier series (DFS) for estimating the 3D shape of a target concurrently with its kinematic state is introduced. Here, the shape is represented as a star-convex radial function, decomposed as spherical DFS. In comparison to ordinary DFS, spherical DFS do not suffer from ambiguities at the poles. Details will be given in the paper. The shape representation is integrated into a Bayesian state estimator framework via a measurement equation. As range sensors only generate measurements from the target side facing the sensor, the shape representation is modified to enable application of shape symmetries during the estimation process. The model is analyzed in simulations and compared to a shape estimation procedure using spherical harmonics. Finally, shape estimation using spherical and ordinary DFS is compared to analyze the effect of the pole problem in extended object tracking (EOT) scenarios.
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.
Introduction. Despite its high accuracy, polysomnography (PSG) has several drawbacks for diagnosing obstructive sleep apnea (OSA). Consequently, multiple portable monitors (PMs) have been proposed. Objective. This systematic review aims to investigate the current literature to analyze the sets of physiological parameters captured by a PM to select the minimum number of such physiological signals while maintaining accurate results in OSA detection. Methods. Inclusion and exclusion criteria for the selection of publications were established prior to the search. The evaluation of the publications was made based on one central question and several specific questions. Results. The abilities to detect hypopneas, sleep time, or awakenings were some of the features studied to investigate the full functionality of the PMs to select the most relevant set of physiological signals. Based on the physiological parameters collected (one to six), the PMs were classified into sets according to the level of evidence. The advantages and the disadvantages of each possible set of signals were explained by answering the research questions proposed in the methods. Conclusions. The minimum number of physiological signals detected by PMs for the detection of OSA depends mainly on the purpose and context of the sleep study. The set of three physiological signals showed the best results in the detection of OSA.
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.
The State of Custom
(2021)
In our article, we engage with the anthropologist Gerd Spittler’s pathbreaking
article “Dispute settlement in the shadow of Leviathan” (1980) in which
he strives to integrate the existence of state courts (the eponymous Leviathan’s
shadow) in (post-)colonial Africa into the analysis on non-state court legal practices.
According to Spittler, it is because of undesirable characteristics inherent
in state courts that the disputing parties tended to rather involve mediators than
pursue a state court judgment. The less people liked state courts, the more likely
they were to (re-)turn to dispute settlement procedures. Now how has this situation
changed in the last four decades since its publication date? We relate his findings
to contemporary debates in legal anthropology that investigate the relationship
between disputing, law and the state. We also show through our own work in
Africa and Asia, particularly in Southern Ethiopia and Kyrgyzstan, in what ways
Spittler’s by now classical contribution to the field of legal anthropology in 1980
can be made fruitful for a contemporary anthropology of the state at a time when
not only (legal) anthropology has changed, but especially the way states deal with
putatively “customary” forms of dispute settlement.
Bittamo
(2021)
The Ethiopian state increasingly seeks to enlist putative ‘traditional authorities’ to lend legitimacy to policies and interventions in the southwestern peripheries of the country. The underlying assumptions do not accord with the perceptions of the local populations: among the Kara in the South Omo region, legitimacy is predicated upon duty and accountability, and higher degrees of public legitimacy are disconnected from authority and direct command over other people’s conduct. The office of the Kara bitti, the highest spiritual leader, thus proves intractable to such attempts at enlistment and has been little affected by the radical transformation of the Kara’s lives through increasing integration into the Ethiopian state over recent decades. But even as the office has changed little, the lives of those expected to assume the role of bitti has, and the duties of a bitti strongly constrain the office holder and limit their personal ambitions and participation in politics at the local, regional and national level.
Electricity generation from renewable energies often fluctuates due to weather and other natural effects. The instrument of control energy (balancing energy) can compensate for these fluctuations and thus guarantee the system and supply security of the electricity grid. Luxury hotels on tourist islands could react to fluctuations in electricity generation and provide balancing energy. The purpose of this paper is to investigate the electricity consumption of luxury hotels to assess their potential as a source for providing control energy.
The main objective of this paper is to revisit Temursho’s (2020) article “On the Euro method” in a critical and constructive way. We have praised part of his work and at the same time, we have analysed some of his arguments against the Euro method and against the work published by Valderas-Jaramillo et al. (2019). Moreover, we have analysed some other relevant aspects of the SUT-Euro and SUT-RAS methods not covered in Temursho (2020). Temursho (2020) seems to conclude that no one should use the Euro method again because of its limitations and drawbacks. However, although not being the Euro method perfect, we are afraid that there is still space for the use of the Euro method in updating/regionalizing supply and use tables.
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.
Background:
One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment.
Objective:
This paper aims to review the feasibility of blockchain technology for telemedicine.
Methods:
The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex).
Results:
Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%).
Conclusions:
These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.
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.
This paper introduces the concept of Universal Memory Automata (UMA) and automated compilation of Verilog Hardware Description Language (HDL) code at Register Transfer Level (RTL) from UMA graphs for digital designs. The idea is based on the observation that Push Down Automata (PDA) are able to process the Dyk-Language - commonly known as the balanced bracket problem - with a finite set of states while Finite State Machines (FSM) require an infinite set of states. Since infinite sets of states are not applicable to real designs, PDAs appear promising for types of problems similar to the Dyk-Language. PDAs suffer from the problem that complex memory operations need to be emulated by a specific stack management. The presented UMA therefore extends the PDA by other types of memory, e.g. Queue, RAM or CAM. Memories that are eligible for UMAs are supposed to have at least one read and one write port and a one-cycle read/write latency. With their modified state-transfer- and output-function, UMAs are able to operate user-defined numbers, configurations and types of memories. Proof of concept is given by an implementation of a cache coherency protocol, i.e. a practical problem in microprocessor design.
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.
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.
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.
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.
A nonlinear mathematical model for the dynamics of permanent magnet synchronous machines with interior magnets is discussed. The model of the current dynamics captures saturation and dependency on the rotor angle. Based on the model, a flatness-based field-oriented closed-loop controller and a feed-forward compensation of torque ripples are derived. Effectiveness and robustness of the proposed algorithms are demonstrated by simulation results.
The present work proposes the use of modern ICT technologies such as smartphones, NFCs, internet, and web technologies, to help patients in carrying out their therapies. The implemented system provides a calendar with a reminder of the assumptions, ensures the drug identification through NFC, allows remote assistance from healthcare staff and family members to check and manage the therapy in real-time. The system also provides centralized information on the patient's therapeutic situation, helpful in choosing new compatible therapies.
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.
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.
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.
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.
Urban car-free mobility
(2021)
Across the globe, urban areas experience the phenomena of rising road-congestion, air pollution and car accidents. These are just a few popular quantified effects that arise due to rapid, uncoordinated urbanization on a car-centric city layout. There is an urgent need to consider new concepts of urban mobility development to combat these negative effects. Car-free mobility is one notion adopted in diverse formats by numerous cities to create a more inclusive, just, healthy and sustainable urban life. The focus of this thesis is to ex- amine whether a car-free mobility concept is applicable to the Maun Science Park, Bot- swana. Therefore, the idea of car-free mobility, its positive aspects as well as its con- straints, are described first. This illustrates the complexity of urban transport planning as it is intertwined with urban land-use, political vision and people’s perceptions and behav- iors. Secondly, examples and strategies on how to change existing structures are pre- sented. Following this, the smart developments in the field of sustainable urban mobility are considered to provide an insight into their assets and drawbacks. Then the local mo- bility conditions are examined before the car-free concept is exemplarily applied to the Maun Science Park via scenario construction. These scenarios give a first vision of how a car-free concept can be applied to the MSP and additionally provide a starting point for future strategic planning as well as inspiration for other cities to follow along.
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.
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.
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.
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.
Traditional Western philosophy, cognitive science and traditional HCI frameworks approach the term digital and its implications with an implicit dualism (nature/cul-ture, theory /practice, body/mind, human/machine). What lies between is a feature of our postmodern times, in which different states, conditions or positions merge and co-exist in a new, hybrid reality, a “continuous beta” (Mühlenbeck & Skibicki, 2007) version of becoming .Post-digitality involves the physical dimensions of spatio-temporal engagements. This new ontological paradigm reconceptualizes digital technology through the ex-perience of the human body and its senses, thus emphasizing form-taking, situation-al engagement and practice rather than symbolic, disembodied rationality. This rais-es two questions in particular: how to encourage curiosity, playfulness, serendipity, emergence, discourse and collectivity? How to construct working methods without foregrounding and dividing the subject into an individual that already takes posi-tion? This paper briefly outlines the rhizomatic framework that I developed within my PhD research. This attempts to overcome two prevailing tendencies: first, the one-sided view of scientific approaches to knowledge acquisition and the pure-ly application-oriented handling of materials, technologies and machines; second, the distanced perception of the world. In contrast, my work involves project-driven alchemic curiosity and doing research through artistic design practice. This means thinking through materials, technologies and machinic interactions. Now, at the end of this PhD journey, 10 interdisciplinary projects have emerged from this ontological queer-paradigm that is post-digital–crafting 4.0. Below I illustrate this approach and its outcomes.
Production and marketing of cereal grains are some of the main activities in developing countries to ensure food security. However, the food gap is complicated further by high postharvest loss of grains during storage. This study aimed to compare low‐cost modified‐atmosphere hermetic storage structures with traditional practice to minimize quantitative and qualitative losses of grains during storage. The study was conducted in two phases: in the first phase, seven hermetic storage structures with or without smoke infusion were compared, and one selected structure was further validated at scaled‐up capacity in the second phase.
In modern fruit processing technology, non-destructive quality measuring techniques aresought for determining and controlling changes in the optical, structural, and chemical properties of theproducts. In this context, changes inside the product can be measured during processing. Especiallyfor industrial use, fast, precise, but robust methods are particularly important to obtain high-qualityproducts. In this work, a newly developed multi-spectral imaging system was implemented andadapted for drying processes. Further it was investigated if the system could be used to link changesin the surface spectral reflectance during mango drying with changes in moisture content andcontents of chemical components. This was achieved by recovering the spectral reflectance frommulti-spectral image data and comparing the spectral changes with changes of the total soluble solids(TSS), pH-value and the relative moisture contentxwbof the products. In a first step, the camera wasmodified to be used in drying, then the changes in the spectra and quality criteria during mangodrying were measured. For this, mango slices were dried at air temperatures of 40–80◦C and relativeair humidities of 5%–30%. Samples were analyzed and pictures were taken with the multi-spectralimaging system. The quality criteria were then predicted from spectral data. It could be shown thatthe newly developed multi-spectral imaging system can be used for quality control in fruit drying.There are strong indications as well, that it can be employed for the prediction of chemical qualitycriteria of mangoes during drying. This way, quality changes can be monitored inline during theprocess using only one single measuring device.
Location-aware mobile devices are becoming increasingly popular and GPS sensors are built into nearly every portable unit with computational capabilities. At the same time, the emergence of location-aware virtual services and ideas calls for new efficient spatial real-time queries. Communication latency in mobile environments interacting with high decentralization and the need of scalability in high-density systems with immense client counts leads to major challenges. In this paper we describe a decentralized architecture for continuous range queries in settings in which both, the requested and the requesting clients, are mobile. While prior works commonly use a request-response approach we provide a stream-based adaptive grid solution dealing with arbitrary high client counts and improving communication latency that meets given hard real-time constraints.
A residual neural network was adapted and applied to the Physionet/Computing data in Cardiology Challenge 2020 to detect 24 different classes of cardiac abnormalities from 12-lead. Additive Gaussian noise, signal shifting, and the classification of signal sections of different lengths were applied to prevent the network from overfitting and facilitating generalization. Due to the use of a global pooling layer after the feature extractor, the network is independent of the signal’s length. On the hidden test set of the challenge, the model achieved a validation score of 0.656 and a full test score of 0.27, placing us 15th out of 41 officially ranked teams (Team name: UC_Lab_Kn). These results show the potential of deep neural networks for ap- plication to raw data and a complex multi-class multi-label classification problem, even if the training data is from di- verse datasets and of differing lengths.
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.
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.
What drives entrepreneurial action to create a lasting impact? The creation of new ventures that aim at having an impact beyond their financial performance face additional challenges: achieving economic sustainability and at the same time addressing social or environmental issues. Little is known on how these new hybrid organizations, aiming for multiple impact dimensions, manage to be congruent with their blended values. A dataset of 4,125 early-stage ventures is used to gain insights into how blended values are converted into financial, social and environmental impacts, giving shape to different types of hybrid organizations. Our findings suggest new hybrid organizations might opt to sacrifice financial impact to achieve social impact, yet this is not the case when they aim to generate environmental or sustainable impact. Therefore, the tensions and sacrifices related to holding blended values are not homogeneous across all types of new hybrid organizations.
Uncertainty about the future requires companies to create discontinuous innovations. Established companies, however, struggle to do so; whereas independent startups seem to better cope with this. Consequently, established companies set up entrepreneurial initiatives to make use of startups' benefits. Consequently, this led-amongst others-to great interest in socalled corporate entrepreneurship (CE) programs and to the development and characterization of several different forms. Their processes to achieve certain objectives, yet, are still rather ineffective. Thus, considerations of the actions performed in preparation for and during CE programs could be one approach to improve this but are still absent today. Furthermore, the increasing use of several CE programs in parallel seems to bear the potential for synergies and, thus, more efficient use of resources. Aiming to provide insights to both issues, this study analyzes actions of CE programs, by looking at interviews with managers of seven corporate incubators and accelerator programs of five established German tech-companies.
In today's volatile world, established companies must be capable of optimizing their core business with incremental innovations while simultaneously developing discontinuous innovations to maintain their long-term competitiveness. Balancing both is a major challenge for companies, since different types of innovation require different organizational structures, operational modes and management styles. Established companies tend to excel in improving their current business through incremental innovations which are closely related to their current knowledge base and competencies. However, this often goes hand in hand with challenges in the exploration of knowledge that is new to the company and that is essential for the development of discontinuous innovations. In this respect, the concept of corporate entrepreneurship is recognized as a way to strengthen the exploration of new knowledge and to support the development of discontinuous innovation. For managing corporate entrepreneurship more effectively, it is crucial to understand which types of knowledge can be created through corporate entrepreneurship and which organizational designs are more suited to gain certain types of knowledge. To answer these questions, this study analyzed 23 semi-structured interviews conducted with established companies that are running such entrepreneurial activities. The results show (1) that three general types of knowledge can be explored through corporate entrepreneurship and (2) that some organizational designs are more suited to explore certain knowledge types than others are.
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.
We provide an overview of the ongoing discussions on the objectives of the energy transition in the form of a conceptual framework, intending to facilitate the search for the most viable options for a successful transformation of the energy system. For this purpose, we examine the development of energy policy goals in Germany in the past and present, whereby we give an overview of objectives and assessment approaches from politics, economics, and science. Moreover, we then merge the different views into a common framework and analyze the central conflict between the wholeness of a hypothetical target circle and the simplification in favor of a hypothetical target point in more detail.
This paper proposes a novel transmission scheme for generalized multistream spatial modulation. This new approach uses one Mannheim error correcting codes over Gaussian or Eisenstein integers as multidimensional signal constellations. These codes enable a suboptimal decoding strategy with near maximum likelihood performance for transmission over the additive white Gaussian noise channel. In this contribution, this decoding algorithm is generalized to the detection for generalized multistream spatial modulation. The proposed method can outperform conventional generalized multistream spatial modulation with respect to decoding performance, detection complexity, and spectral efficiency.
Soft-input decoding of concatenated codes based on the Plotkin construction and BCH component codes
(2020)
Low latency communication requires soft-input decoding of binary block codes with small to medium block lengths.
In this work, we consider generalized multiple concatenated (GMC) codes based on the Plotkin construction. These codes are similar to Reed-Muller (RM) codes. In contrast to RM codes, BCH codes are employed as component codes. This leads to improved code parameters. Moreover, a decoding algorithm is proposed that exploits the recursive structure of the concatenation. This algorithm enables efficient soft-input decoding of binary block codes with small to medium lengths. The proposed codes and their decoding achieve significant performance gains compared with RM codes and recursive GMC decoding.
The reliability of flash memories suffers from various error causes. Program/erase cycles, read disturb, and cell to cell interference impact the threshold voltages and cause bit errors during the read process. Hence, error correction is required to ensure reliable data storage. In this work, we investigate the bit-labeling of triple level cell (TLC) memories. This labeling determines the page capacities and the latency of the read process. The page capacity defines the redundancy that is required for error correction coding. Typically, Gray codes are used to encode the cell state such that the codes of adjacent states differ in a single digit. These Gray codes minimize the latency for random access reads but cannot balance the page capacities. Based on measured voltage distributions, we investigate the page capacities and propose a labeling that provides a better rate balancing than Gray labeling.
Side Channel Attack Resistance of the Elliptic Curve Point Multiplication using Eisenstein Integers
(2020)
Asymmetric cryptography empowers secure key exchange and digital signatures for message authentication. Nevertheless, consumer electronics and embedded systems often rely on symmetric cryptosystems because asymmetric cryptosystems are computationally intensive. Besides, implementations of cryptosystems are prone to side-channel attacks (SCA). Consequently, the secure and efficient implementation of asymmetric cryptography on resource-constrained systems is demanding. In this work, elliptic curve cryptography is considered. A new concept for an SCA resistant calculation of the elliptic curve point multiplication over Eisenstein integers is presented and an efficient arithmetic over Eisenstein integers is proposed. Representing the key by Eisenstein integer expansions is beneficial to reduce the computational complexity and the memory requirements of an SCA protected implementation.
In this article, we give the construction of new four-dimensional signal constellations in the Euclidean space, which represent a certain combination of binary frequency-shift keying (BFSK) and M-ary amplitude-phase-shift keying (MAPSK). Description of such signals and the formulas for calculating the minimum squared Euclidean distance are presented. We have developed an analytic building method for even and odd values of M. Hence, no computer search and no heuristic methods are required. The new optimized BFSK-MAPSK (M = 5,6,···,16) signal constructions are built for the values of modulation indexes h =0.1,0.15,···,0.5 and their parameters are given. The results of computer simulations are also provided. Based on the obtained results we can conclude, that BFSK-MAPSK systems outperform similar four-dimensional systems both in terms of minimum squared Euclidean distance and simulated symbol error rate.
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.
Deep neural networks (DNNs) are known for their high prediction performance, especially in perceptual tasks such as object recognition or autonomous driving. Still, DNNs are prone to yield unreliable predictions when encountering completely new situations without indicating their uncertainty. Bayesian variants of DNNs (BDNNs), such as MC dropout BDNNs, do provide uncertainty measures. However, BDNNs are slow during test time because they rely on a sampling approach. Here we present a single shot MC dropout approximation that preserves the advantages of BDNNs without being slower than a DNN. Our approach is to analytically approximate for each layer in a fully connected network the expected value and the variance of the MC dropout signal. We evaluate our approach on different benchmark datasets and a simulated toy example. We demonstrate that our single shot MC dropout approximation resembles the point estimate and the uncertainty estimate of the predictive distribution that is achieved with an MC approach, while being fast enough for real-time deployments of BDNNs.
Probabilistic Deep Learning
(2020)
Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications.
At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a quantification of the reliability of automated image analysis is essential, in particular in medicine when physicians rely on the results for making critical treatment decisions. In this work, we provide an entire framework to diagnose ischemic stroke patients incorporating Bayesian uncertainty into the analysis procedure. We present a Bayesian Convolutional Neural Network (CNN) yielding a probability for a stroke lesion on 2D Magnetic Resonance (MR) images with corresponding uncertainty information about the reliability of the prediction. For patient-level diagnoses, different aggregation methods are proposed and evaluated, which combine the individual image-level predictions. Those methods take advantage of the uncertainty in the image predictions and report model uncertainty at the patient-level. In a cohort of 511 patients, our Bayesian CNN achieved an accuracy of 95.33% at the image-level representing a significant improvement of 2% over a non-Bayesian counterpart. The best patient aggregation method yielded 95.89% of accuracy. Integrating uncertainty information about image predictions in aggregation models resulted in higher uncertainty measures to false patient classifications, which enabled to filter critical patient diagnoses that are supposed to be closer examined by a medical doctor. We therefore recommend using Bayesian approaches not only for improved image-level prediction and uncertainty estimation but also for the detection of uncertain aggregations at the patient-level.
Mapping of tree seedlings is useful for tasks ranging from monitoring natural succession and regeneration to effective silvicultural management. Development of methods that are both accurate and cost-effective is especially important considering the dramatic increase in tree planting that is required globally to mitigate the impacts of climate change. The combination of high-resolution imagery from unmanned aerial vehicles and object detection by convolutional neural networks (CNNs) is one promising approach. However, unbiased assessments of these models and methods to integrate them into geospatial workflows are lacking. In this study, we present a method for rapid, large-scale mapping of young conifer seedlings using CNNs applied to RGB orthomosaic imagery. Importantly, we provide an unbiased assessment of model performance by using two well-characterised trial sites together containing over 30,000 seedlings to assemble datasets with a high level of completeness. Our results showed CNN-based models trained on two sites detected seedlings with sensitivities of 99.5% and 98.8%. False positives due to tall weeds at one site and naturally regenerating seedlings of the same species led to slightly lower precision of 98.5% and 96.7%. A model trained on examples from both sites had 99.4% sensitivity and precision of 97%, showing applicability across sites. Additional testing showed that the CNN model was able to detect 68.7% of obscured seedlings missed during the initial annotation of the imagery but present in the field data. Finally, we demonstrate the potential to use a form of weakly supervised training and a tile-based processing chain to enhance the accuracy and efficiency of CNNs applied to large, high-resolution orthomosaics.
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.
Three-dimensional ship localization with only one camera is a challenging task due to the loss of depth information caused by perspective projection. In this paper, we propose a method to measure distances based on the assumption that ships lie on a flat surface. This assumption allows to recover depth from a single image using the principle of inverse perspective. For the 3D ship detection task, we use a hybrid approach that combines image detection with a convolutional neural network, camera geometry and inverse perspective. Furthermore, a novel calculation of object height is introduced. Experiments show that the monocular distance computation works well in comparison to a Velodyne lidar. Due to its robustness, this could be an easy-to-use baseline method for detection tasks in navigation systems.
Creative industry and cultural tourism destination Lake Constance - a media discourse analysis
(2020)
The following media discourse analysis examines the news media coverage of four regional online newspapers, about the topics “creative industries” and “cultural tourism” at Lake Constance region in the period from 2006 until 2016. The results show that, besides event-relater reporting, there is currently no vibrant media discourse on the topics “creative industries” and “cultural tourism”. Even though the image of the Lake Constance region is heavily influenced by tourism, “cultural tourism” also plays a secondary role when it comes to regional news reporting. Moreover, discourses do not overlap and thus no synergies within the local media discourse are formed. This result is relevant for the regional tourism development, because the cooperation between “creative industries” and “cultural tourism” creates opportunities such as the expansion of the tourism offer and an extension of the tourist season. To activate unused opportunities at the different destinations of the region, a supra-regional visibility of the sector “creative industries” should be developed and the cooperation of the sector with local stakeholders of cultural tourism should be promoted.
This paper presents the goals, service design approach, and the results of the project “Accessible Tourism around Lake Constance”, which is currently run by different universities, industrial partners and selected hotels in Switzerland, Germany and Austria. In the 1st phase, interviews with different persons with disabilities and elderly persons have been conducted to identify the barriers and pains faced by tourists who want to spend their holidays in the region of Lake Constance as well as possible assistive technologies that help to overcome these barriers. The analysis of the interviews shows that one third of the pains and barriers are due to missing, insufficient, wrong or inaccessible information about the
accessibility of the accommodation, surroundings, and points of interests during the planning phase of the holidays. Digital assistive technologies hence play a
major role in bridging this information gap. In the 2nd phase so-called Hotel-Living-Labs (HLL) have been established where the identified assistive technologies
can be evaluated. Based on these HLLs an overall service for accessible holidays has been designed and developed. In the last phase, this service has been implemented
based on the HLLs as well as the identified assistive technologies and is currently field tested with tourists with disabilities from the three participated countries.
Digital technology and architecture have become inseparable, with new approaches and methodologies not just affecting the workflows and practice of architects but shaping the very character of architecture.
This compendious work offers a wide-ranging orientation to the new landscape with its opportunities, its challenges, and its vast potential.
Shared Field, Divided Field
(2020)
A conceptual framework for indigenous ecotourism projects – a case study in Wayanad, Kerala, India
(2020)
This paper analyses indigenous ecotourism in the Indian district of Wayanad, Kerala, using a conceptual framework based on a PATA 2015 study on indigenous tourism that includes the criteria: human rights, participation, business and ecology. Detailed indicator sets for each criterion are applied to a case study of the Priyadarshini Tea Environs with a qualitative research approach addressing stakeholders from the public sector, non-governmental organisations, academia, tour operators and communities including Adivasi and non-Adivasi. In-depth interviews were supported by participant and non-participant observations. The authors adapted this framework to the needs of the case study and consider that this modified version is a useful tool for academics and practitioners wishing to evaluate and develop indigenous ecotourism projects. The results show that the Adivasi involved in the Priyadarshini Tea Environs project benefit from indigenous ecotourism. But they could profit more if they had more involvement in and control of the whole tourism value chain.
The Lake Constance region is due to its scenic attractiveness one of the most visited destinations in German-speaking countries. Scenic attractiveness as well as so-called landscape stereotypes also play a decisive role in tourism marketing. Tour operators reproduce supra-individual landscape concepts and establish mental geographies that ultimately influence the choice of destinations. A growing trend in tourism is the emergence of creative narratives in tourism marketing and tourism offers induced by creative companies. By means of a discourse-analytical investigation, whose theoretical and conceptual frame of reference is the hegemony and discourse theory of Laclau and Mouffe (1985), recurring landscape stereotypes are identified in tourist promotional material for the destination Bodensee. Based on these results as well as expert interviews with regional tourism stakeholders, a discussion of the creative economic potential for regional tourism marketing will take place. The investigation shows that these potentials are currently not being exhausted. At the same time, creative tourism can help a rural region, such as Lake Constance, to position itself as an alternative to city tourism, while at the same time addressing the lucrative target group 60plus.
The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense by hand the entire underwater infrastructure. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we propose to scan the above and underwater port structure with a Multi-SensorSystem, and -by a fully automated processto classify the obtained point cloud into damaged and undamaged zones. We make use of simulated training data to test our approach since not enough training data with corresponding class labels are available yet. To that aim, we build a rasterised heightfield of a point cloud of a sheet pile wall by cutting it into verticall slices. The distance from each slice to the corresponding line generates the heightfield. This latter is propagated through a convolutional neural network which detects anomalies. We use the VGG19 Deep Neural Network model pretrained on natural images. This neural network has 19 layers and it is often used for image recognition tasks. We showed that our approach can achieve a fully automated, reproducible, quality-controlled damage detection which is able to analyse the whole structure instead of the sample wise manual method with divers. The mean true positive rate is 0.98 which means that we detected 98 % of the damages in the simulated environment.
Modeling a suitable birth density is a challenge when using Bernoulli filters such as the Labeled Multi-Bernoulli (LMB) filter. The birth density of newborn targets is unknown in most applications, but must be given as a prior to the filter. Usually the birth density stays unchanged or is designed based on the measurements from previous time steps.
In this paper, we assume that the true initial state of new objects is normally distributed. The expected value and covariance of the underlying density are unknown parameters. Using the estimated multi-object state of the LMB and the Rauch-Tung-Striebel (RTS) recursion, these parameters are recursively estimated and adapted after a target is detected.
The main contribution of this paper is an algorithm to estimate the parameters of the birth density and its integration into the LMB framework. Monte Carlo simulations are used to evaluate the detection driven adaptive birth density in two scenarios. The approach can also be applied to filters that are able to estimate trajectories.
In this thesis, the recognition problem and the properties of eigenvalues and eigenvectors of matrices which are strictly sign-regular of a given order, i.e., matrices whose minors of a given order have the same strict sign, are considered. The results are extended to matrices which are sign-regular of a given order, i.e., matrices whose minors of a given order have the same sign or are allowed to vanish. As a generalization, a new type of matrices called oscillatory of a specific order, are introduced. Furthermore, the properties for this type are investigated. Also, same applications to dynamic systems are given.
The expansion of a given multivariate polynomial into Bernstein polynomials is considered. Matrix methods for the calculation of the Bernstein expansion of the product of two polynomials and of the Bernstein expansion of a polynomial from the expansion of one of its partial derivatives are provided which allow also a symbolic computation.
Totally nonnegative matrices, i.e., matrices having all their minors nonnegative, and matrix intervals with respect to the checkerboard partial order are considered. It is proven that if the two bound matrices of such a matrix interval are totally nonnegative and satisfy certain conditions, then all matrices from this interval are also totally nonnegative and satisfy the same conditions.
Let A = [a_ij] be a real symmetric matrix. If f:(0,oo)-->[0,oo) is a Bernstein function, a sufficient condition for the matrix [f(a_ij)] to have only one positive eigenvalue is presented. By using this result, new results for a symmetric matrix with exactly one positive eigenvalue, e.g., properties of its Hadamard powers, are derived.
We propose and apply a requirements engineering approach that focuses on security and privacy properties and takes into account various stakeholder interests. The proposed methodology facilitates the integration of security and privacy by design into the requirements engineering process. Thus, specific, detailed security and privacy requirements can be implemented from the very beginning of a software project. The method is applied to an exemplary application scenario in the logistics industry. The approach includes the application of threat and risk rating methodologies, a technique to derive technical requirements from legal texts, as well as a matching process to avoid duplication and accumulate all essential requirements.
We present source code patterns that are difficult for modern static code analysis tools. Our study comprises 50 different open source projects in both a vulnerable and a fixed version for XSS vulnerabilities reported with CVE IDs over a period of seven years. We used three commercial and two open source static code analysis tools. Based on the reported vulnerabilities we discovered code patterns that appear to be difficult to classify by static analysis. The results show that code analysis tools are helpful, but still have problems with specific source code patterns. These patterns should be a focus in training for developers.
For a long time, the use of intermediate products in production has been growing more rapidly in most countries than domestic production. This is a strong indication of more interdependency in production. The main purpose of input-output analysis is to study the interdependency of industries in an economy. Often the term interindustry analysis is also used. Therefore, the exchange of intermediate products is a key issue of input-output analysis. We will use input–output data for this study that the author prepared for the new ‘Handbook on Supply, Use and Input–Output Tables with Extensions and Applications’ of the United Nations. The supply use and input–output tables contain separate valuation matrices for trade margins, transport margins, value added tax, other taxes on products and subsidies on products. For the study, two input–output models were developed to evaluate the impact of fuel subsidy and taxation reform on output, gross domestic product, inflation and trade. Six scenarios are discussed covering different aspects of the reform.
The evaluation of the effectiveness of different machine learning algorithms on a publicly available database of signals derived from wearable devices is presented with the goal of optimizing human activity recognition and classification. Among the wide number of body signals we choose a couple of signals, namely photoplethysmographic (optically detected subcutaneous blood volume) and tri-axis acceleration signals that are easy to be simultaneously acquired using commercial widespread devices (e.g. smartwatches) as well as custom wearable wireless devices designed for sport, healthcare, or clinical purposes. To this end, two widely used algorithms (decision tree and k-nearest neighbor) were tested, and their performance were compared to two new recent algorithms (particle Bernstein and a Monte Carlo-based regression) both in terms of accuracy and processing time. A data preprocessing phase was also considered to improve the performance of the machine learning procedures, in order to reduce the problem size and a detailed analysis of the compression strategy and results is also presented.
Good sleep is crucial for a healthy life of every person. Unfortunately, its quality often decreases with aging. A common approach to measuring the sleep characteristics is based on interviews with the subjects or letting them fill in a daily questionnaire and afterward evaluating the obtained data. However, this method has time and personal costs for the interviewer and evaluator of responses. Therefore, it would be important to execute the collection and evaluation of sleep characteristics automatically. To do that, it is necessary to investigate the level of agreement between measurements performed in a traditional way using questionnaires and measurements obtained using electronic monitoring devices. The study presented in this manuscript performs this investigation, comparing such sleep characteristics as "time going to bed", "total time in bed", "total sleep time" and "sleep efficiency". A total number of 106 night records of elderly persons (aged 65+) were analyzed. The results achieved so far reveal the fact that the degree of agreement between the two measurement methods varies substantially for different characteristics, from 31 minutes of mean difference for "time going to bed" to 77 minutes for "total sleep time". For this reason, a direct exchange of objective and subjective measuring methods is currently not possible.
Polysomnography is a gold standard for a sleep study, and it provides very accurate results, but its cost (both personnel and material) are quite high. Therefore, the development of a low-cost system for overnight breathing and heartbeat monitoring, which provides more comfort while recording the data, is a well-motivated challenge. The system proposed in this manuscript is based on the usage of resistive pressure sensors installed under the mattress. These sensors can measure slight pressure changes provoked during breathing and heartbeat. The captured signal requires advanced processing, like applying filters and amplifiers before the analog signal is ready for the next step. Then, the output signal is digitalized and further processed by an algorithm that performs a custom filtering before it can recognize breathing and heart rate in real-time. The result can be directly visualized. Furthermore, a CSV file is created containing the raw data, timestamps, and unique IDs to facilitate further processing. The achieved results are promising, and the average deviation from a reference device is about 4bpm.
Cardiovascular diseases are directly or indirectly responsible for up to 38.5% of all deaths in Germany and thus represent the most frequent cause of death. At present, heart diseases are mainly discovered by chance during routine visits to the doctor or when acute symptoms occur. However, there is no practical method to proactively detect diseases or abnormalities of the heart in the daily environment and to take preventive measures for the person concerned. Long-term ECG devices, as currently used by physicians, are simply too expensive, impractical, and not widely available for everyday use. This work aims to develop an ECG device suitable for everyday use that can be worn directly on the body. For this purpose, an already existing hardware platform will be analyzed, and the corresponding potential for improvement will be identified. A precise picture of the existing data quality is obtained by metrological examination, and corresponding requirements are defined. Based on these identified optimization potentials, a new ECG device is developed. The revised ECG device is characterized by a high integration density and combines all components directly on one board except the battery and the ECG electrodes. The compact design allows the device to be attached directly to the chest. An integrated microcontroller allows digital signal processing without the need for an additional computer. Central features of the evaluation are a peak detection for detecting R-peaks and a calculation of the current heart rate based on the RR interval. To ensure the validity of the detected R-peaks, a model of the anatomical conditions is used. Thus, unrealistic RR-intervals can be excluded. The wireless interface allows continuous transmission of the calculated heart rate. Following the development of hardware and software, the results are verified, and appropriate conclusions about the data quality are drawn. As a result, a very compact and wearable ECG device with different wireless technologies, data storage, and evaluation of RR intervals was developed. Some tests yelled runtimes up to 24 hours with wireless Lan activated and streaming.
In previous studies, we used a method for detecting stress that was based exclusively on heart rate and ECG for differentiation between such situations as mental stress, physical activity, relaxation, and rest. As a response of the heart to these situations, we observed different behavior in the Root Mean Square of the Successive differences heartbeats (RMSSD). This study aims to analyze Virtual Reality via a virtual reality headset as an effective stressor for future works. The value of the Root Mean Square of the Successive Differences is an important marker for the parasympathetic effector on the heart and can provide information about stress. For these measurements, the RR interval was collected using a breast belt. In these studies, we can observe the Root Mean Square of the successive differences heartbeats. Additional sensors for the analysis were not used. We conducted experiments with ten subjects that had to drive a simulator for 25 minutes using monitors and 25 minutes using virtual reality headset. Before starting and after finishing each simulation, the subjects had to complete a survey in which they had to describe their mental state. The experiment results show that driving using virtual reality headset has some influence on the heart rate and RMSSD, but it does not significantly increase the stress of driving.
This work is a study about a comparison of survey tools and it should help developers in selecting a suited tool for application in an AAL environment. The first step was to identify the basic required functionality of the survey tools used for AAL technologies and to compare these tools by their functionality and assignments. The comparative study was derived from the data obtained, previous literature studies and further technical data. A list of requirements was stated and ordered in terms of relevance to the target application domain. With the help of an integrated assessment method, the calculation of a generalized estimate value was performed and the result is explained. Finally, the planned application of this tool in a running project is explained.
This paper presents the implementation of deep learning methods for sleep stage detection by using three signals that can be measured in a non-invasive way: heartbeat signal, respiratory signal, and movement signal. Since signals are measurements taken during the time, the problem is seen as time-series data classification. Deep learning methods are chosen to solve the problem are convolutional neural network and long-short term memory network. Input data is structured as a time-series sequence of mentioned signals that represent 30 seconds epoch, which is a standard interval for sleep analysis. The records used belong to the overall 23 subjects, which are divided into two subsets. Records from 18 subjects were used for training the data and from 5 subjects for testing the data. For detecting four sleep stages: REM (Rapid Eye Movement), Wake, Light sleep (Stage 1 and Stage 2), and Deep sleep (Stage 3 and Stage 4), the accuracy of the model is 55%, and F1 score is 44%. For five stages: REM, Stage 1, Stage 2, Deep sleep (Stage 3 and 4), and Wake, the model gives an accuracy of 40% and F1 score of 37%.
Due to its economic size, economic policy measures, in particular trade policies, have a far‐reaching impact on global economic developments. This chapter quantifies the economic consequences of US protectionist trade aspirations. It focuses on trade policy scenarios, which have been communicated by the current US administration as potential new trade policies. The chapter draws on the results of a study of the ifo Institute conducted on behalf of the Bertelsmann Foundation. In the first simulation, a retraction from the North American Free Trade Agreement is considered. The chapter then illustrates the potential consequences of a “border tax adjustment” policy. It also simulates further measures to protect the US market by presuming an increase in American duties. The chapter presents robust quantitative results that can be expected if an increasingly protectionist US trade policy were to be implemented.
This article introduces the Global Sanctions Data Base (GSDB), a new dataset of economic sanctions that covers all bilateral, multilateral, and plurilateral sanctions in the world during the 1950–2016 period across three dimensions: type, political objective, and extent of success. The GSDB features by far the most cases amongst data bases that focus on effective sanctions (i.e., excluding threats) and is particularly useful for analysis of bilateral international transactional data (such as trade flows). We highlight five important stylized facts: (i) sanctions are increasingly used over time; (ii) European countries are the most frequent users and African countries the most frequent targets; (iii) sanctions are becoming more diverse, with the share of trade sanctions falling and that of financial or travel sanctions rising; (iv) the main objectives of sanctions are increasingly related to democracy or human rights; (v) the success rate of sanctions has gone up until 1995 and fallen since then. Using state-of-the-art gravity modeling, we highlight the usefulness of the GSDB in the realm of international trade. Trade sanctions have a negative but heterogeneous effect on trade, which is most pronounced for complete bilateral sanctions, followed by complete export sanctions.
The Montgomery multiplication is an efficient method for modular arithmetic. Typically, it is used for modular arithmetic over integer rings to prevent the expensive inversion for the modulo reduction. In this work, we consider modular arithmetic over rings of Gaussian integers. Gaussian integers are subset of the complex numbers such that the real and imaginary parts are integers. In many cases Gaussian integer rings are isomorphic to ordinary integer rings. We demonstrate that the concept of the Montgomery multiplication can be extended to Gaussian integers. Due to independent calculation of the real and imaginary parts, the computation complexity of the multiplication is reduced compared with ordinary integer modular arithmetic. This concept is suitable for coding applications as well as for asymmetric key cryptographic systems, such as elliptic curve cryptography or the Rivest-Shamir-Adleman system.
In this work, we investigate a hybrid decoding approach that combines algebraic hard-input decoding of binary block codes with soft-input decoding. In particular, an acceptance criterion is proposed which determines the reliability of a candidate codeword. For many received codewords the stopping criterion indicates that the hard-decoding result is sufficiently reliable, and the costly soft-input decoding can be omitted. The proposed acceptance criterion significantly reduces the decoding complexity. For simulations we combine the algebraic hard-input decoding with ordered statistics decoding, which enables near maximum likelihood soft-input decoding for codes of small to medium block lengths.