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Die Erfindung betrifft in einem ersten Aspekt eine Vorrichtung zur transkutanen Aufbringung eines elektrischen Stimulationsreizes auf ein Ohr. Die Vorrichtung umfasst einen Schaltungsträger, mindestens zwei Elektroden sowie eine Steuerungseinheit, wobei die Steuerungseinheit dazu konfiguriert ist, anhand von Stimulationsparametern ein elektrisches Stimulationssignal an den Elektroden zu erzeugen. Dabei ist die Vorrichtung, insbesondere eine Oberfläche des Schaltungsträgers der Vorrichtung, auf eine anatomische Form eines Ohres angepasst, sodass Elektroden auf der Oberfläche des Schaltungsträgers aufgebracht sind und ausgewählte Bereiche des Ohres kontaktieren Die Vorrichtung ist dadurch kennzeichnet, dass diese weiterhin einen Sensor zur Erkennung mindestens eines physiologischen Parameter umfasst und eine Steuerungseinheit dazu konfiguriert ist, anhand des mindestens einen physiologischen Parameters die Stimulationsparameter für den Stimulationsreiz anzupassen.In einem weiteren Aspekt betrifft die Erfindung ein Verfahren zur Herstellung der erfindungsgemäßen Vorrichtung.
Die Erfindung betrifft ein Verfahren zum Maximieren der von einer analogen Entropiequelle abgeleiteten Entropie, wobei das Verfahren folgende Schritte aufweist:- Bereitstellen von Eingabedaten für die analoge Entropiequelle (2);- Erzeugen von Rückgabewerten durch die analoge Entropiequelle basierend auf den Eingabedaten (3); und- Gruppieren der Rückgabewerte, wobei das Gruppieren der Rückgabewerte ein Anwenden von Versätzen auf Rückgabewerte aufweist (4).
Blockchain interoperability: the state of heterogenous blockchain-to-blockchain communication
(2023)
Blockchain technology has been increasingly adopted over the past few years since the introduction of Bitcoin, with several blockchain architectures and solutions being proposed. Most proposed solutions have been developed in isolation, without a standard protocol or cryptographic structure to work with. This has led to the problem of interoperability, where solutions running on different blockchain platforms are unable to communicate, limiting the scope of use. With blockchains being adopted in a variety of fields such as the Internet of Things, it is expected that the problem of interoperability if not addressed quickly, will stifle technology advancement. This paper presents the current state of interoperability solutions proposed for heterogenous blockchain systems. A look is taken at interoperability solutions, not only for cryptocurrencies, but also for general data-based use cases. Current open issues in heterogenous blockchain interoperability are presented. Additionally, some possible research directions are presented to enhance and to extend the existing blockchain interoperability solutions. It was discovered that though there are a number of proposed solutions in literature, few have seen real-world implementation. The lack of blockchain-specific standards has slowed the progress of interoperability. It was also realized that most of the proposed solutions are developed targeting cryptocurrency-based applications.
This paper presents an overview of EREMI, a two-year project funded under ERASMUS+ KA203, and its results. The project team’s main objective was to develop and validate an advanced interdisciplinary higher education curriculum, which includes lifelong learning components. The curriculum focuses on enhancing resource efficiency in the manufacturing industry and optimising poorly or non-digitised industrial physical infrastructure systems. The paper also discusses the results of the project, highlighting the successful achievement of its goals. EREMI effectively supports the transition to Industry 5.0 by preparing a common European pool of future experts. Through comprehensive research and collaboration, the project team has designed a curriculum that equips students with the necessary skills and knowledge to thrive in the evolving manufacturing landscape. Furthermore, the paper explores the significance of EREMI’s contributions to the field, emphasising the importance of resource efficiency and system optimisation in industrial settings. By addressing the challenges posed by under-digitised infrastructure, the project aims to drive sustainable and innovative practices in manufacturing. All five project partner organisations have been actively engaged in offering relevant educational content and framework for decentralised sustainable economic development in regional and national contexts through capacity building at a local level. A crucial element of the added value is the new channel for obtaining feedback from students. The survey results, which are outlined in the paper, offer valuable insights gathered from students, contributing to the continuous improvement of the project.
Training deep neural networks using backpropagation is very memory and computationally intensive. This makes it difficult to run on-device learning or fine-tune neural networks on tiny, embedded devices such as low-power micro-controller units (MCUs). Sparse backpropagation algorithms try to reduce the computational load of on-device learning by training only a subset of the weights and biases. Existing approaches use a static number of weights to train. A poor choice of this so-called backpropagation ratio limits either the computational gain or can lead to severe accuracy losses. In this paper we present TinyProp, the first sparse backpropagation method that dynamically adapts the back-propagation ratio during on-device training for each training step. TinyProp induces a small calculation overhead to sort the elements of the gradient, which does not significantly impact the computational gains. TinyProp works particularly well on fine-tuning trained networks on MCUs, which is a typical use case for embedded applications. For typical datasets from three datasets MNIST, DCASE2020 and CIFAR10, we are 5 times faster compared to non-sparse training with an accuracy loss of on average 1%. On average, TinyProp is 2.9 times faster than existing, static sparse backpropagation algorithms and the accuracy loss is reduced on average by 6 % compared to a typical static setting of the back-propagation ratio.
Security in IT systems, particularly in embedded devices like Cyber Physical Systems (CPSs), has become an important matter of concern as it is the prerequisite for ensuring privacy and safety. Among a multitude of existing security measures, the Transport Layer Security (TLS) protocol family offers mature and standardized means for establishing secure communication channels over insecure transport media. In the context of classical IT infrastructure, its security with regard to protocol and implementation attacks has been subject to extensive research. As TLS protocols find their way into embedded environments, we consider the security and robustness of implementations of these protocols specifically in the light of the peculiarities of embedded systems. We present an approach for systematically checking the security and robustness of such implementations using fuzzing techniques and differential testing. In spite of its origin in testing TLS implementations we expect our approach to likewise be applicable to implementations of other cryptographic protocols with moderate efforts.
In dem Maße, in dem sich die industrielle Automatisierung verändert, verändern sich auch die Anforderungen an die Sicherheit. Neben der funktionalen Sicherheit rückt dabei immer mehr die Datensicherheit in den Mittelpunkt. Als „best practice“ bietet es sich an, bewährte Sicherungstechniken aus der IT auch in der industriellen Kommunikation einzusetzen.
Während neue Komponenten für „Short Range Wireless Networks“ längere Zeit eher moderate technische Fortschritte gebracht haben, sind in jüngerer Zeit einige außerordentlich interessante strategische Entwicklungslinien deutlich geworden, die in diesem Beitrag an Hand von konkreten Produktbeispielen vorgestellt werden.
Deep learning approaches are becoming increasingly important for the estimation of the Remaining Useful Life (RUL) of mechanical elements such as bearings. This paper proposes and evaluates a novel transfer learning-based approach for RUL estimations of different bearing types with small datasets and low sampling rates. The approach is based on an intermediate domain that abstracts features of the bearings based on their fault frequencies. The features are processed by convolutional layers. Finally, the RUL estimation is performed using a Long Short-Term Memory (LSTM) network. The transfer learning relies on a fixed-feature extraction. This novel deep learning approach successfully uses data of a low-frequency range, which is a precondition to use low-cost sensors. It is validated against the IEEE PHM 2012 Data Challenge, where it outperforms the winning approach. The results show its suitability for low-frequency sensor data and for efficient and effective transfer learning between different bearing types.
The importance of machine learning has been increasing dramatically for years. From assistance systems to production optimisation to support the health sector, almost every area of daily life and industry comes into contact with machine learning. Besides all the benefits that ML brings, the lack of transparency and the difficulty in creating traceability pose major risks. While there are solutions that make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge. Unnoticed modification of a model is also a danger when using ML. One solution is to create an ML birth certificate and an ML family tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.