Refine
Year of publication
Document Type
- Conference Proceeding (28)
- Article (reviewed) (16)
- Article (unreviewed) (11)
- Patent (3)
- Contribution to a Periodical (1)
Conference Type
- Konferenzartikel (26)
- Konferenz-Abstract (1)
- Sonstiges (1)
Is part of the Bibliography
- yes (59)
Keywords
- Blockchain (5)
- blockchain (4)
- Internet of Things (3)
- Eingebettetes System (2)
- Energieversorgung (2)
- IIoT (2)
- IT-Sicherheit (2)
- Intelligentes Stromnetz (2)
- Kommunikation (2)
- Neural networks (2)
- Scalability (2)
- Security (2)
- Sensortechnik (2)
- Sicherheit (2)
- Sicherheitstechnik (2)
- efficient training (2)
- machine learning (2)
- 5G (1)
- Android (1)
- Aurikuläre Stimulation (1)
- Automation (1)
- Automotive engineering (1)
- Blockchain-to-Blockchain communication (1)
- Blockchains (1)
- CIoT (1)
- Car-to-Car-(C2C)-Communication (1)
- Cellular networks (1)
- Cloud computing (1)
- Cloud storage (1)
- Communication (1)
- Cyber Physical Systems, (1)
- Deep learning (1)
- Edge AI (1)
- Elektroden-Interface (1)
- Embedded AI (1)
- Embedded Software (1)
- Embedded Systems (1)
- Entropie (1)
- Fahrzeug (1)
- Federated Learning (1)
- Feldbus (1)
- Heuristic algorithms (1)
- Higher Education (1)
- Industrial internet of things (1)
- Industrie 4.0 (1)
- Industry 4.0 (1)
- Industry automation (1)
- Internet der Dinge (1)
- Interoperability (1)
- IoT security (1)
- LPWAN (1)
- Low-latency (1)
- MEMS (1)
- Machine learning (1)
- Machine-to- Machine-(M2M)-Communication (1)
- Monitoring (1)
- NB-IoT (1)
- Network Test (1)
- Netzwerk (1)
- Niedrige Energie (1)
- PUF key generation (1)
- Particle swarm optimization (1)
- Patient (1)
- Predictive Maintenance (1)
- Produktion (1)
- Prozessor (1)
- Reinforcement learning (1)
- Simulation (1)
- Smart Grid (1)
- Smart Metering (1)
- TLS (1)
- TTCN3 (1)
- Testumgebung (1)
- TinyML (1)
- Traceability (1)
- URLLC (1)
- Umwelt (1)
- Unsupervised Learning (1)
- V2X (1)
- VANET (1)
- Variational Autoencoders (1)
- Vehicle safety (1)
- Wasserstand (1)
- Wireless Body Area Networks (1)
- accelerometer (1)
- algorithm-based data analysis (1)
- analog physical unclonable function system (1)
- authentication (1)
- authorization (1)
- benchmarking (1)
- blockchain-based system (1)
- cluster (1)
- compression (1)
- cryptography (1)
- cybersecurity (1)
- dickkopf 3 (1)
- distributed ledger (1)
- eingebettetes System (1)
- game theory (1)
- gossip protocol (1)
- gyroscope (1)
- inertial measurement unit (1)
- integer linear programming (1)
- intermediate domain (1)
- legacy machines (1)
- lifelong learning (1)
- maintenance (1)
- manufacturing industries (1)
- network optimization (1)
- peer-to-peer (1)
- physically unclonable function (PUF) (1)
- predictive maintenance (1)
- quality feedback survey and results assessment (1)
- remaining useful life (1)
- resource efficiency (1)
- scalability (1)
- security (1)
- security keys (1)
- sharding algorithm (1)
- shop floor (1)
- sparse backpropagation (1)
- storage efficiency (1)
- storage optimization (1)
- summarization (1)
- syndrome coding (1)
- temperature dependency (1)
- topology (1)
- transfer learning (1)
- trust management (1)
- trust management system (1)
- unique interdisciplinary international higher education approach (1)
Institute
- ivESK - Institut für verlässliche Embedded Systems und Kommunikationselektronik (35)
- Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019) (33)
- Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) (24)
- Fakultät Medien (M) (ab 22.04.2021) (1)
- Zentrale Einrichtungen (1)
Open Access
- Open Access (59) (remove)
This article deals with the problem of wireless synchronization between onboard computing devices of small-sized unmanned aerial vehicles (SUAV) equipped with integrated wireless chips (IWC). Accurate synchronization between several devices requires the precise timestamping of batches transmitting and receiving on each of them. The best precision is demonstrated by those solutions where timestamping is performed on the PHY level, right after modulation/demodulation of the batch. Nowadays, most of the currently produced IWC are Systems-on-a-Chip (SoC) that include both PHY and MAC, implemented with one or several processor cores application. SoC allows create more cost and energy efficient wireless devices. At the same time, it limits the developers direct access to the internal signals and significantly complicates precise timestamping for sent and received batches, required for mutual synchronization of industrial devices. Some modern IEEE 802.11 IWCs have inbuilt functions that use internal chip clock to register timestamps. However, high jitter of the interfaces between the external device and IWC degrades the comparison of the timestamps from the internal clock to those registered by external devices. To solve this problem, the article proposes a novel approach to the synchronization, based on the analysis of IWC receiver input potential. The benefit of this approach is that there is no need to demodulate and decode the received batches, thus allowing it implementation with low-cost IWCs. In this araticle, Cypress CYW43438 was taken as an example for designing hardware and software solutions for synchronization between two SUAV onboard computing devices, equipped with IWC. The results of the performed experimental studies reveal that mutual synchronization error of the proposed method does not exceed 10 μs.
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).
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 CAN bus still is an important fieldbus in various domains, e.g. for in-car communication or automation applications. To counter security threats and concerns in such scenarios we design, implement, and evaluate the use of an end-to-end security concept based on the Transport Layer Security protocol. It is used to establish authenticated, integrity-checked, and confidential communication channels between field devices connected via CAN. Our performance measurements show that it is possible to use TLS at least for non time-critical applications, as well as for generic embedded networks.
Wireless communication systems more and more become part of our daily live. Especially with the Internet of Things (IoT) the overall connectivity increases rapidly since everyday objects become part of the global network. For this purpose several new wireless protocols have arisen, whereas 6LoWPAN (IPv6 over Low power Wireless Personal Area Networks) can be seen as one of the most important protocols within this sector. Originally designed on top of the IEEE802.15.4 standard it is a subject to various adaptions that will allow to use 6LoWPAN over different technologies; e.g. DECT Ultra Low Energy (ULE). Although this high connectivity offers a lot of new possibilities, there are several requirements and pitfalls coming along with such new systems. With an increasing number of connected devices the interoperability between different providers is one of the biggest challenges, which makes it necessary to verify the functionality and stability of the devices and the network. Therefore testing becomes one of the key components that decides on success or failure of such a system. Although there are several protocol implementations commonly available; e.g., for IoT based systems, there is still a lack of according tools and environments as well as for functional and conformance testing. This article describes the architecture and functioning of the proposed test framework based on Testing and Test Control Notation Version 3 (TTCN-3) for 6LoWPAN over ULE networks.
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.
Die zunehmende Anzahl von Transistoren mit immer kleineren Strukturgrößen führt zu einer zunehmenden Leistungsaufnahme in modernen Prozessoren. Das gilt insbesondere für High-End Prozessoren, die mit einer hohen Taktfrequenz betrieben werden. Die aufgenommene Leistung wird in Wärme umgewandelt, die in einer Temperaturerhöhung der Prozessoren resultiert. Hohe Betriebstemperaturen verursachen u.a. eine verringerte Rechenleistung, eine kürzere Lebensdauer des Prozessors und höhere Leckströme. Aus diesen Gründen wird aktives, dynamisches thermisches Management immer wichtiger. Dieser Beitrag stellt eine Erweiterung zu dem Standard- Linux-Scheduler in der Kernel-Version 3.0 für eingebettete Systeme vor: einen PID-Regler, der unter Angabe einer Solltemperatur eine dynamische Frequenz- und Spannungsskalierung durchführt. Die Experimente auf dem Freescale LMX6 Quadcore-Prozessor zeigen, dass der PID-Regler die Betriebstemperatur des Prozessors an die Solltemperatur regeln kann. Er ist die Grundlage für eine in Zukunft zu entwickelnde prädiktive Regelung.
The overview of public key infrastructure based security approaches for vehicular communications
(2015)
Modern transport infrastructure becomes a full member of globally connected network. Leading vehicle manufacturers have already triggered development process, output of which will open a new horizon of possibilities for consumers and developers by providing a new communication entity - a car, thus enabling Car2X communications. Nevertheless some of available systems already provide certain possibilities for vehicles to communicate, most of them are considered not sufficiently secured. During last 15 years a number of big research projects funded by European Union and USA governments were started and concluded after which a set of standards were published prescribing a common architecture for Car2X and vehicles onboard communications. This work concentrates on combining inner and outer vehicular communications together with a use of Public Key Infrastructure (PKI).
The Internet of Things (IoT), ubiquitous computing and ubiquitous connectivity, Cyber Physical Systems (CPS), ambient intelligence, Machine-to-Machine communication (M2M) or Car-to-Car (C2C)-communication, smart metering, smart grid, telematics, telecare, telehealth – there are many buzzwords around current developments related to the Internet.
This contribution gives an overview on such IoT-applications, as they are already used today to improve the availability of information, increase efficiency, push system limits and extend the value chain. At a closer look, the economic and technical development can be separated into different phases. It is interesting that we are currently at the threshold to a new phase, with decentralized and cooperative communication and control nodes as cornerstones. Thus, embedded systems and their connectivity are in the middle of the scene.
This recent development is described along with some example projects from the author’s team which are used in industrial automation, energy supply and distribution (home automation and smart metering), traffic engineering (cooperative driver assistance systems), and in telehealth and telecare.
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.