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Disturbances of the cardiac conduction system causing reentry mechanisms above the atrioventricular (AV) node are induced by at least one accessory pathway with different conducting properties and refractory periods. This work aims to further develop the already existing and continuously expanding Offenburg heart rhythm model to visualise the most common supraventricular reentry tachycardias to provide a better understanding of the cause of the respective reentry mechanism.
The Go programming language is an increasingly popular language but some of its features lack a formal investigation. This article explains Go's resolution mechanism for overloaded methods and its support for structural subtyping by means of translation from Featherweight Go to a simple target language. The translation employs a form of dictionary passing known from type classes in Haskell and preserves the dynamic behavior of Featherweight Go programs.
A Hybrid Optoelectronic Sensor Platform with an Integrated Solution‐Processed Organic Photodiode
(2021)
Hybrid systems, unifying printed electronics with silicon‐based technology, can be seen as a driving force for future sensor development. Especially interesting are sensing elements based on printed devices in combination with silicon‐based high‐performance electronics for data acquisition and communication. In this work, a hybrid system integrating a solution‐processed organic photodiode in a silicon‐based system environment, which enables flexible device measurement and application‐driven development, is presented. For performance evaluation of the integrated organic photodiode, the measurements are compared to a silicon‐based counterpart. Therefore, the steady state response of the hybrid system is presented. Promising application scenarios are described, where a solution‐processed organic photodiode is fully integrated in a silicon system.
Cryptographic protection of messages requires frequent updates of the symmetric cipher key used for encryption and decryption, respectively. Protocols of legacy IT security, like TLS, SSH, or MACsec implement rekeying under the assumption that, first, application data exchange is allowed to stall occasionally and, second, dedicated control messages to orchestrate the process can be exchanged. In real-time automation applications, the first is generally prohibitive, while the second may induce problematic traffic patterns on the network. We present a novel seamless rekeying approach, which can be embedded into cyclic application data exchanges. Although, being agnostic to the underlying real-time communication system, we developed a demonstrator emulating the widespread industrial Ethernet system PROFINET IO and successfully use this rekeying mechanism.
In recent years, physically unclonable functions (PUFs) have gained significant attraction in IoT security applications, such as cryptographic key generation and entity authentication. PUFs extract the uncontrollable production characteristics of different devices to generate unique fingerprints for security applications. When generating PUF-based secret keys, the reliability and entropy of the keys are vital factors. This study proposes a novel method for generating PUF-based keys from a set of measurements. Firstly, it formulates the group-based key generation problem as an optimization problem and solves it using integer linear programming (ILP), which guarantees finding the optimum solution. Then, a novel scheme for the extraction of keys from groups is proposed, which we call positioning syndrome coding (PSC). The use of ILP as well as the introduction of PSC facilitates the generation of high-entropy keys with low error correction costs. These new methods have been tested by applying them on the output of a capacitor network PUF. The results confirm the application of ILP and PSC in generating high-quality keys.
The following describes a new method for estimating the parameters of an interior permanent magnet synchronous machine (IPMSM). For the estimation of the parameters the current slopes caused by the switching of the inverter are used to determine the unknowns of the system equations of the electrical machine. The angle and current dependence of the machine parameters are linearized within a PWM cycle. By considering the different switching states of the inverter, several system equations can be derived and a solution can be found within one PWM cycle. The use of test signals and filter-based approaches is avoided. The derived algorithm is explained and validated with measurements on a test bench.
Time-Sensitive Networking (TSN) is the most promising time-deterministic wired communication approach for industrial applications. To extend TSN to "IEEE 802.11" wireless networks two challenging problems must be solved: synchronization and scheduling. This paper is focused on the first one. Even though a few solutions already meet the required synchronization accuracies, they are built on expensive hardware that is not suited for mass market products. While next Wi-Fi generation might support the required functionalities, this paper proposes a novel method that makes possible high-precision wireless synchronization using commercial low-cost components. With the proposed solution, a standard deviation of synchronization error of less than 500 ns can be achieved for many use cases and system loads on both CPU and network. This performance is comparable to modern wired real-time field busses, which makes the developed method a significant contribution for the extension of the TSN protocol to the wireless domain.
Most eCommerce applications, like web-shops have millions of products. In this context, the identification of similar products is a common sub-task, which can be utilized in the implementation of recommendation systems, product search engines and internal supply logistics. Providing this data set, our goal is to boost the evaluation of machine learning methods for the prediction of the category of the retail products from tuples of images and descriptions.
It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time.
This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.