An atmospheric front is an imaginary surface that separates two distinct air masses and is commonly defined as the warm-air side of a frontal zone with high gradients of atmospheric temperature and humidity (Fig. 1, left). These fronts are a widely used conceptual model in meteorology, which are often encountered in the literature as two-dimensional (2D) front lines on surface analysis charts. This paper presents a method for computing three-dimensional (3D) atmospheric fronts as surfaces that is capable of extracting continuous and well-confined features suitable for 3D visual analysis, spatiotemporal tracking, and statistical analyses (Fig. 1, middle, right). Recently developed contour-based methods for 3D front extraction rely on computing the third derivative of a moist potential temperature field. Additionally, they require the field to be smoothed to obtain continuous large-scale structures. This paper demonstrates the feasibility of an alternative method to front extraction using ridge surface computation. The proposed method requires only the second derivative of the input field and produces accurate structures even from unsmoothed data. An application of the ridge-based method to a data set corresponding to Cyclone Friederike demonstrates its benefits and utility towards visual analysis of the full 3D structure of fronts.

Technological trends in different domains have en-abled operators to integrate large numbers of sensors within their wireless communication networks. These sensors can provide data for many different fields of applications, e.g. process monitoring, eHealth or position feedback for closed loop control of autonomous vehicles. However, such systems often face the challenge to aggregate and process information from many distinct sources in a homogeneous and coherent manner. We propose a software architecture design that is based on any context management and message handling system. The general objective is to design a common interface for different information sources. We assume that by processing this data in conjunction it can be possible to obtain enhanced quality of information. The presented abstract architecture can enable the user to flexibly integrate different Information Anchors - active sensors or passive tags - within a comprehensive multi-modal, multi-domain data aggregation platform. By applying a system as proposed, it is possible to generate a pool of homogeneous data sets that can be used as a sound foundation for further analysis. We demonstrate the feasibility of the concept by presenting a possible realization of a system as described in form of an inter-operable multi-technology indoor localization system.

Increasing heterogeneity of industrial network systems is a fact and the chances that in the future one communication standard will be able to fulfill the requirements of all possible applications are utopian. With the increasing number of communication systems, their management, configuration, and maintenance become a significant issue. Additionally, due to the increasing amount of network services and traffic, the management of available network resources and the possibility of delivering certain levels of communication quality of service, especially across different network solutions becomes a big challenge. Therefore, in this paper a Controller of Controllers (CoC) concept for management of heterogeneous industrial networks is proposed. The concept is designed to support still widely spread legacy fieldbus systems, different Ethernetbased industrial solutions, and potentially upcoming network technologies. The goal is achieved by leveraging state-of-theart architectural concepts such as software-defined networks, which allows for integration of abstract models in network management. The concept is described and discussed by way of a demonstrator concept for a heterogeneous system of fieldbus and Ethernet-based time-sensitive networks.

The overall Industry 4.0 (I4.0) developments combined with the disruptive process of IT-based digitalisation create a vast amount of new opportunities but also challenges for the industrial automation domain. The combination of hybrid (wired & wireless) communication architectures, already widely installed legacy technologies, new approaches, such as Time-Sensitive Networking (TSN) or 5G, and the general heterogeneity of the industrial landscape results in a high configuration complexity. This creates the necessity for future-proof industrial communication network management systems. Therefore, this paper summarises the current state of the art in this area in order to identify the specific requirements towards future industrial network management systems. The most promising candidate is the Software-Defined Networking (SDN) concept. To evaluate SDN as a possible enabler, specified industrial requirements are compared with the current technological and conceptual capabilities of SDN. In addition, drawbacks resulting in future research questions are identified.

Photoplethysmography has been used in a wide range of medical devices for measuring oxygen saturation, cardiac output, assessing autonomic function, and detecting peripheral vascular disease. Artifacts can render the photoplethysmogram (PPG) useless. Thus, algorithms capable of identifying artifacts are critically important. However, the published PPG algorithms are limited in algorithm and study design. Therefore, the authors developed a novel embedded algorithm for real-time pulse waveform (PWF) segmentation and artifact detection based on a contour analysis in the time domain. This paper provides an overview about PWF and artifact classifications, presents the developed PWF analysis, and demonstrates the implementation on a 32-bit ARM core microcontroller. The PWF analysis was validated with data records from 63 subjects acquired in a sleep laboratory, ergometry laboratory, and intensive care unit in equal parts. The output of the algorithm was compared with harmonized experts' annotations of the PPG with a total duration of 31.5 h. The algorithm achieved a beat-to-beat comparison sensitivity of 99.6%, specificity of 90.5%, precision of 98.5%, and accuracy of 98.3%. The interrater agreement expressed as Cohen's kappa coefficient was 0.927 and as F-measure was 0.990. In conclusion, the PWF analysis seems to be a suitable method for PPG signal quality determination, real-time annotation, data compression, and calculation of additional pulse wave metrics such as amplitude, duration, and rise time.