Distributed Ledger Technology for the systematic Investigation and Reduction of Information Asymmetry in Collaborative Networks

Costs, risks and inefficiencies in Collaborative Networks (CNs) resulting from information asymmetries have been discussed in the scientific community for years. In this work, supply chain networks, as common representative of CNs, are used as object of investigation. Therein, problems and requirements of interorganizational information exchange are elaborated as well as the potential role Distributed Ledger Technology (DLT) could play to address them. As major challenge, convincing all relevant network partners to resolve asymmetric information by sharing sensitive data is identified. To face this issue, the value of shared information is prioritized as a motivational aspect. Finally, we propose a search process to systematically assess the benefits of information sharing in collaborative networks. To coordinate and implement this process regarding the derived requirements of CNs we propose system components based on DLT design patterns

Data Sovereignty in Data Donation Cycles - Requirements and Enabling Technologies for the Data-driven Development of Health Applications

Personalized healthcare is expected to increase the efficiency and the effectiveness of health services using different kinds of algorithms on existing data. This approach is currently confronted with the lack of digital data and the desire for self-determined personal data handling. However, the issue of health data donation is on the political agenda of some governments. Within this work, a knowledge base will be created by reviewing existing approaches and technologies regarding this topic with the focus on chronic diseases. A list of requirements will be derived from which we conceptualize a data donation cycle to demonstrate the challenges and opportunities of health data sovereignty and its future possibilities concerning data-driven health application development. By linking the requirements to technological approaches, the baseline for future open ecosystems will be presented

Decision model to design a blockchain-based system for storing sensitive health data

The storage and sharing of sensitive health data in Blockchain-based systems implicates data protection issues that must be addressed when designing such systems. Those issues can be traced back to the properties of decentralized systems. A blessing but also a curse in the context of health data is the transparency of the Blockchain, because it allows the stored data to be viewed by all participants of the network. In addition, the property of immutability is in contrast to the possibility to delete the personal data upon request according to the European General Data Protection Regulation (GDPR). Accordingly, approaches to tackle these issues have recently been discussed in research and industry, e.g. by storing sensitive data encrypted On-Chain or Off-Chain on own servers connected to a Blockchain. These approaches deal with how the confidentiality and integrity of stored data can be guaranteed and how data can be deleted. By reviewing the proposed approaches, we develop a taxonomy to summarize their specific technical characteristics and create a decision model that will allow the selection of a suitable approach for the design of future Blockchain-based systems for the storage of sensitive health data. Afterwards, we demonstrate the utility of the decision model based on a use case for storing test results from a digital dementia screening application. The paper concludes with a discussion of the results and suggestions for future research

Multiplexed Holographic Combiner with Extended Eye Box Fabricated by Wave Front Printing

We present an array-based volume holographic optical element (vHOE) recorded as an optical combiner for novel display applications such as smart glasses. The vHOE performs multiple, complex optical functions in the form of large off-axis to on-axis wave front transformations and an extended eye box implemented in the form of two distinct vertex points with red and green chromatic functions. The holographic combiner is fabricated by our extended immersion-based wave front printing setup, which provides extensive prototyping capabilities due to independent wave front modulation and large possible off-axis recording angles, enabling vHOEs in reflection with a wide range of different recording configurations. The presented vHOE is build up as an array of sub-holograms, where each element is recorded with individual optical functions. We introduce a design and fabrication method to combine two angular and two spectral functions in the volume grating of individual sub-holograms, demonstrating complex holographic elements with four multiplexed optical functions comprised in a single layer of photopolymer film. The introduced design and fabrication process allows the precise tuning of the vHOE’s diffractive properties to achieve well-balanced diffraction efficiencies and angular distributions between individual multiplexed functions

Receding horizon control (RHC) on a Lidar based preview controller design for the active wind turbine pitch system

In this paper, we present a design of a 2DOF (Degree Of Freedom) RHC/FB (FeedBack) control method for the pitch system of wind turbines based on the preview wind speed measurement by a Lidar system

A Deep Learning First Approach to Remaining Useful Lifetime Prediction of Filtration System With Improved Response to Changing Operational Parameters Using Parameterized Fully-connected Layer

For the remaining useful lifetime prediction, apart from the normal sensor data which is updated regularly, there are also operational parameters, which do not change during a cycle of operation. Different sets of parameters result in essentially different, but relevant systems and thus require the adaptation from the statistical model for better prediction. We noticed that neural networks could easily overfit into one set of operational parameters and demonstrate constant bias in the prediction for other sets (underfitting). An aspect of major contribution from our work is the use of Parameterized Fully-Connected Layer (PFL). The PFL builds the parameter dependency right into each layer, in this way the parameters act as ”meta-inputs” which adapt the model of neural network models to the different operating conditions. In another aspect of contribution, our work demonstrated that, instead of using feature engineering, convolutional layers could be used to automatically learn the features which are relevant for the prediction. In this way, the deep learning architecture could be reused for different problems or systems. We conduct experiments on the filtration system datasets provided by the Data Challenge 2020 and received results that compare favorably to the prize winners

Model-Driven Dementia Prevention and Intervention Platform

Most types of dementia, including Alzheimer’s disease, are not curable. However, there are risk factors, such as obesity or hypertension, that can promote the development of dementia. Holistic treatment of these risk factors can prevent the onset of dementia or delay it in its early stages. To support individualized treatment of risk factors in dementia, this paper presents a model-driven digital platform. It enables monitoring of biomarkers using smart devices from the internet of medical things (IoMT) for the target group. The collected data from such devices can be used to optimize and adjust treatment in a patient in the loop manner. To this end, providers such as Google Fit and Withings have been connected to the platform as example data sources. To achieve treatment and monitoring data interoperability with existing medical systems, internationally accepted standards such as FHIR are used. The configuration and control of the personalized treatment processes are achieved using a self-developed domain-specific language. For this language, an associated diagram editor was implemented, which allows the management of the treatment processes through graphical models. This graphical representation should help treatment providers to understand and manage these processes more easily. To investigate this hypothesis, a usability study was conducted with twelve participants. We were able to show that such graphical representations provide advantages in clarity in reviewing the system, but lack in easy set-up (compared to wizard-style systems)

Weather Influence and Classification with Automotive Lidar Sensors

Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithms are trained to only detect moving and static objects as well as ground estimation, but intentionally ignore weather effects to reduce false detections. In this work, we present an in-depth analysis of automotive lidar performance under harsh weather conditions, i.e. heavy rain and dense fog. An extensive data set has been recorded for various fog and rain conditions, which is the basis for the conducted in-depth analysis of the point cloud under changing environmental conditions. In addition, we introduce a novel approach to detect and classify rain or fog with lidar sensors only and achieve an mean union over intersection of 97.14 % for a data set in controlled environments. The analysis of weather influences on the performance of lidar sensors and the weather detection are important steps towards improving safety levels for autonomous driving in adverse weather conditions by providing reliable information to adapt vehicle behavior.Comment: 8 pages, will be published in the IEEE IV 2019 Proceeding

When Data Fly: An Open Data Trading System in Vehicular Ad Hoc Networks

Communication between vehicles and their environment (i.e., vehicle-to-everything or V2X communication) in vehicular ad hoc networks (VANETs) has become of particular importance for smart cities. However, economic challenges, such as the cost incurred by data sharing (e.g., due to power consumption), hinder the integration of data sharing in open systems into smart city applications, such as dynamic environmental zones. Moving from open data sharing to open data trading can address the economic challenges and incentivize vehicle drivers to share their data. In this context, integrating distributed ledger technology (DLT) into open systems for data trading is promising for reducing the transaction cost of payments in data trading, avoiding dependencies on third parties, and guaranteeing openness. However, because the integration of DLT conflicts with the short available communication time between fast moving objects in VANETs, it remains unclear how open data trading in VANETs using DLT should be designed to be viable. In this work, we present a system design for data trading in VANETs using DLT. We measure the required communication time for data trading between a vehicle and a roadside unit in a real scenario and estimate the associated cost. Our results show that the proposed system design is technically feasible and economically viable