Visible Light Positioning (VLP) systems offer a sophisticated solution to achieve indoor positioning. Utilizing the existing LED (light emitting diode) lighting infrastructure along with an optical receiver, the position of an object or an individual can be determined. In this work we give an overview of the design process of our own VLP system with the goal of creating an easily adoptable VLP system that utilizes conventional LED light and design a compact optical receiver unit for it. We demonstrate how frequency modulated LEDs, enable LED identification for the applied positioning algorithm, while remaining unobtrusive to room occupants. This work is intended to give reasoning to our design choices and to project future enhancements for the VLP system, both in terms of software and hardware.

Decarbonizing the mobility and heating sector involves increasing connected components in low-voltage grids. The simulation of distribution grids and the incorporation of an energy system are relevant instruments for evaluating the effects of these developments. However, grids are highly diversified, and with over 900,000 low-voltage grids in Germany, the simulation would require significant data management and computing capacity. A solution already applied in the literature is the simulation of representative grids. Here, we show the compatibility of clusters and representatives for grid topologies from the literature and further extend and validate them by applying accurate grid data. Our analysis indicates that clusters from the literature unify well across three key parameters but also reveals that the clusters still exclude a relevant amount of grids. Extension, reclassification, and validation using about 1200 real grids establish meta-clusters covering the spectrum of grids from rural to urban regions, focusing on residential to commercial supply tasks. We anticipate our assay to be a further relevant step toward typifying low-voltage distribution grids in Germany.

Joint Communication and Sensing (JCS) emerges as a crucial technology composed to revolutionize forthcoming wireless systems. Acknowledged as both a vital technology and a pivotal usage scenario for the anticipated 6th generation (6G) wireless networks, JCS takes advantages of the communication signals to detect and as well monitor the surrounding environment, facilitating sensing, target detection, and localization. However, current JCS technologies meet various challenges demanding comprehensive solutions. These challenges include standardization, privacy, security, interference, energy efficiency, complexity and cost. Advancements in technologies supporting JCS facilitate the seamless integration of sensing and communication capabilities. Key technologies contributing to JCS include visible light communication, millimeter-wave communication, radar systems, sensor fusion and machine learning. This manuscript discusses the fundamental elements of JCS, various approaches, current challenges and prospects for future contributions with particular focus on Radio Frequency RF-JCS, as well as the widely adopted innovative approach based on Visible Light technologies VL-JCS, which promises to contribute significantly to the future of JCS research.

Accurate indoor positioning is becoming increasingly important, especially in highly automated industrial environments with robots. In addition, LED-based lighting is also being used more and more frequently in such application fields. In the present work, the possibility to exploit the LED lighting infrastructure with a novel approach for implementing an accurate indoor positioning system is investigated. For this purpose, a demonstrator luminaire LEDPOS is proposed and evaluated that combines visible light sensing based on backscattered reflections to accurately estimate the two-dimensional position of a retroreflective foil at the floor while providing simultaneously an unimpaired room illumination. In particular, the same LED elements are shared for illumination and for the sensing functionality. Furthermore, the algorithm for data evaluation and position determination is based on a machine learning approach that is implemented on the edge in the luminaire. Thus, the presented approach allows for a simple and cost-efficient implementation in different applications. The experimental characterization of the LEDPOS demonstrator in a real-world scenario shows that a very good positioning accuracy can be achieved, in which the average error for the two-dimensional position of the retroreflective foil within an area of 0.64 m 2 remains in the range of 3 cm.

Visible Light Positioning (VLP) is a promising candidate to enable widespread availability of location-based services in indoor environments. Existing VLP receiver designs achieve satisfactory accuracy, but often neglect cost-effectiveness and size constraints. To address this, we designed an ultra-flat singlet lens with a height of only 20 μm to be utilized in a compact image sensor-based VLP receiver. This specially designed lens brings the advantage of cost-effective manufacturing, high aperture, high field-of-view and short focal length. Compared to conventional cameras used in VLP systems, the presented optical receiver can be used in scenarios where privacy concerns play a critical role, like in wearable devices or in privacy-sensitive environments, as no perceivable image of the scene or the user is formed on the image sensor. With two separate VLP approaches, image processing based triangulation and machine learning assisted fingerprinting, we demonstrate positioning accuracy under 10 cm in an experimental setup. Furthermore, the demonstrator is integrated in an autonomously driving robot that has to avoid a geofence, exemplifying a location-based service.

The energy sector faces a pressing need for significant transformation to curb CO2 emissions. For instance, Czechia and Germany have taken steps to phase out fossil thermal power plants by 2038, opting instead for a greater reliance on variable renewable energy sources like wind and solar power. Nonetheless, thermal power plants will still have roles, too. While the conventional multistage axial turbine design has been predominant in large-scale power plants for the past century, it is unsuitable for small-scale decentralized projects due to complexity and cost. To address this, the study investigates less common turbine types, which were discarded as they demonstrated lower efficiency. One design is the Elektra turbine, characterized by its velocity compounded radial re-entry configuration. The Elektra turbine combines the advantages of volumetric expanders (the low rotational speed requirement) with the advantages of a turbine (no rubbing seals, no lubrication in the working fluid, wear is almost completely avoided). Thus, the research goal of the authors is the implementation of a 10 kW-class ORC turbine driving a cost-effective off-the-shelf 3000 rpm generator. The paper introduces the concept of the Elektra turbine in comparison to other turbines and proposes this approach for an ORC working fluid. In the second part, the 1D design and 3D–CFD optimization of the 7 kW Elektra turbine working with Hexamethyldisiloxane (MM) is performed. Finally, CFD efficiency characteristics of various versions of the Elektra are presented and critically discussed regarding the originally defined design approach. The unsteady CFD calculation of the final Elektra version showed 46% total-to-static isentropic efficiency.

Jeder energetische Umwandlungsprozess ist verlustbehaftet und auch in einer Welt ohne fossile Energieträger fallen weiterhin Abwärmeströme in allen Industriezweigen an. Die sinnvolle Nutzung dieser Quellen stellt einen wichtigen Baustein der Energiewende dar. Für verschiedene Leistungen und Temperaturen haben sich unterschiedliche Technologien etabliert, ein flächendeckender Einsatz scheitert aber häufig an der Wirtschaftlichkeit, der Kompaktheit und der Zuverlässigkeit der Anlagen. Das Forschungsprojekt „KompAct“ widmete sich der techno-ökonomischen Optimierung einer nachhaltigen Anlagentechnologie zur Verstromung von industrieller Hochtemperatur-Abwärme mit Wasser in einem dezentralen Rankine Cycle. Die Ergebnisse des Verbundprojekts werden im Folgenden vorgestellt.

Backscattered Visible Light Sensing (BVLS) has proven to be a viable solution approach for various applications ranging from occupancy estimation to gesture recognition. When solely capturing reflections, the scope of applications of such systems can be significantly expanded through utilizing distinct photonic materials as markers. A markers’ key requirement is that it can be distinguished from the reflections of the surroundings with unique optical properties in the intensity and/or in the spectrum of reflected light, whereas achieving generally valid unique properties is challenging. In this manuscript, we present an approach for utilizing phosphorescent markers in BVLS. Based on measuring the decay time after a light stimulus, the marker is clearly distinguishable from optical reflections of the surroundings. Importantly, a human observer recognizes neither the light stimulus nor the measuring of the decay time since both are above the perception threshold of the human eye. Our real world experiments with Ruthenium and Platinum based markers show that unique marker identification is achieved, including the differentiation between Ruthenium and Platinum markers, up to 80 cm distance between the sender/receiver and the marker. The results substantiate that phosphorescent markers in combination with BVLS have the potential for further advancements and applications.

In order to achieve a drastic reduction in CO2 emissions to limit global warming, it must be possible to globally cover the electricity demand from renewable sources. However, the fluctuating availability of solar and wind energy will nevertheless make it necessary to complement the renewables by thermal power plants. Additionally using e.g., the potentials of waste, biomass or waste heat from industrial processes locally in Organic Rankine Cycle (ORC) power plants is a promising approach to finally achieve a stable and sustainable electricity supply. In the present work, therefore, different variants of a radial, velocity compounded re-entry cantilever turbine (Elektra turbine) are investigated regarding their potential for such applications. The paper concentrates on the fast prototyping of an existing 5 kW air turbine demonstrator, recently developed by the authors, by using the possibilities of additive manufacturing with plastic materials to substitute essential parts of the flow geometry. In this context the approach to implement the plastic parts in the turbine is explained. Experimentally determined efficiency parameters of various 3D-printed deflection channel modifications and a printed plastic wheel made of PA12GB are presented, discussed, and compared to the fully milled metal turbine. This results in a quantification of the additional losses due to the higher inaccuracies and roughness of the printed parts, which can be taken into account in further investigations. Furthermore, the various problems and hurdles that must be observed when using 3D-printed plastic parts in high-speed turbomachines are highlighted. With regard to the rotor wheel, the authors conclude that the use of additively manufactured plastic wheels is only feasible with increased preliminary testing. The time required for this is usually not in proportion to the manufacturing time and costs saved. For the stator parts however, 3D-printing turned out to be a reasonable approach.

Digitalization in the context of the Internet of Things (IoT) will also change and heavily influence the possibilities in the industrial environment. However, in order to realize corresponding wireless sensor networks, conventional technologies can reach their limits, which is why light-based approaches have also attracted increased interest and been investigated in recent years. In the present work, the application of Visible Light Sensing (VLS) is investigated for a frequently occurring task in industry. A rotating shaft is to be monitored online in a non-contact manner with respect to its rotational speed as well as operational distortions that can occur over time. VLS provides possibilities for a corresponding low-cost sensor system of low complexity, which in addition can be implemented in a simple way into the existing lighting infrastructure. The performance of this approach was tested and proven experimentally under varying conditions.

The paper introduces and discusses the new concept of a velocity-compounded, radial entry axial outlet Curtis turbine – called CuRAX. First, the motivation for the new turbine concept is discussed and the consideration which led to the CuRAX architecture is introduced. A 5 kWel air CuRAX turbine demonstrator was designed, following the in-house design procedure, and numerically and experimentally verified. The flow field shows the expected pressure and velocity distribution of a velocitycompounded turbine. However, potential for improvements is also obvious. The experimental results show the superiority of the CuRAX turbine at its design speed of 29,000 rpm with 50% total-to-static isentropic efficiency compared to 46 % of the quasi-impulse cantilever turbine – its direct competitor.

Smart sensor systems are increasingly pervading all kind of application fields such as in industry, ambient assisted living, or lifestyle accessories. In this work, a smart system for position-oriented human fall detection is investigated using various machine-learning algorithms for data processing and evaluation. Data from an inertial measurement unit is combined with data from visible light positioning methods to achieve position-based fall detection. Furthermore, an experimental setup and test methods were created to generate appropriate datasets for this analysis. The classification accuracy is compared with three machine-learning algorithms commonly used for such tasks, which are Decision Tree, Naïve Bayes and Support Vector Machine. It is demonstrated that the combination of data from the two sensor systems can improve the recognition accuracy beyond 99% in the best case.

Sensor networks for the Industrial Internet of Things (IIoT) have become a widespread research topic in recent years, and various technologies have already been proposed and applied for this purpose. The focus of this work is on visible light technologies for localization, sensing, and communication, also known as Visible Light Positioning (VLP), Visible Light Sensing (VLS), and Visible Light Communication (VLC), respectively. The potential of these technologies is discussed with respect to the requirements in sensor networks for IIoT applications (Industry 5.0). Further, an outlook is given on challenges and open topics for future research.

The interactive augmentation of musical instruments to foster self-expressiveness and learning has a rich history. Over the past decades, the incorporation of interactive technologies into musical instruments emerged into a new research field requiring strong collaboration between different disciplines. The workshop "Intelligent Music Interfaces" covers a wide range of musical research subjects and directions, including (a) current challenges in musical learning, (b) prototyping for improvements, (c) new means of musical expression, and (d) evaluation of the solutions.

Im 21. Jahrhundert haben einige Länder als Reaktion auf die Nuklearkatastrophe von Fukushima bereits den Ausstieg aus der Kernkraft beschlossen oder zumindest in Erwägung gezogen. Das Ende der Kohleverstromung ist in einigen Ländern bereits umgesetzt oder wie in Deutschland für 2038 zumindest beschlossen, um den globalen Klimawandel zu verlangsamen. Es stellt sich also die Frage, ob die „Dampfmaschine“, das heißt der Rankine-Kreisprozess noch eine Zukunft hat?

Demand driven control of heating or cooling, has the potential to reduce the energy consumption of buildings to a significant portion. One of the main cornerstones of such a demand driven control is the accurate determination of occupancy in the building or subareas of the building. In recent years many different approaches, founded on various technologies and sensing principles have been used to establish an occupancy detection system. Especially in existing buildings, retrofitting such a system can be associated with high installation effort and cost. Furthermore, some technological approaches, mainly camera-based systems are inflicting concerns in regard to the privacy of the users. In this manuscript, we present and discuss an approach for a lighting fixture that performs person recognition and occupancy determination based on the technology of Visible Light Sensing. In our implemented system by solely capturing reflections of the visible light, caused by bypassing persons, the occupancy, the movement direction and the walking speed can be accurately determined. Based on comprehensive real world experiments we will show how our system, fully integrated into an off-the shelf luminaire, is able to operate in different scenarios and ambient light conditions and only requires minimal installation effort without causing any privacy concerns.

Wrist-worn devices enable access to essential information and they are suitable for a wide range of applications, such as gesture and activity recognition. Wrist-worn devices require appropriate technologies when used in sensitive areas, overcoming vulnerabilities in regard to security and privacy. In this work, we propose an approach to recognize wrist rotation by utilizing Visible Light Communication (VLC) that is enabled by low-cost LEDs in an indoor environment. In this regard, we address the channel model of a VLC communicating wristband (VLCcw) in terms of the following factors. The directionality and the spectral composition of the light and the corresponding spectral sensitivity and the directional characteristics of the utilized photodiode (PD). We verify our VLCcw from the simulation environment by a small-scale experimental setup. Then, we analyze the system when white and RGBW LEDs are used. In addition, we optimized the VLCcw system by adding more receivers for the purpose of reducing the number of LEDs on VLCcw. Our results show that the proposed approach generates a feasible real-world simulation environment.

The rapid development of microsystems technology with the availability of various machine learning algorithms facilitates human activity recognition (HAR) and localization by low-cost and low-complexity systems in various applications related to industry 4.0, healthcare, ambient assisted living as well as tracking and navigation tasks. Previous work, which provided a spatiotemporal framework for HAR by fusing sensor data generated from an inertial measurement unit (IMU) with data obtained by an RGB photodiode for visible light sensing (VLS), already demonstrated promising results for real-time HAR and room identification. Based on these results, we extended the system by applying feature extraction methods of the time and frequency domain to improve considerably the correct determination of common human activities in industrial scenarios in combination with room localization. This increases the correct detection of activities to over 90% accuracy. Furthermore, it is demonstrated that this solution is applicable to real-world operating conditions in ambient light.

Monitoring and analyzing basic human daily life activities will help in enhancing the quality of life for both healthy and physically handicapped people. The recognition of sit-to-stand and stand-to-sit transitions in activities of daily living (ADL) is complex task. This is due to the intricate body’s movements during such postural transition. This work proposes a novel method for detecting sit-to-stand and stand-to-sit postural transitions in addition to other human physical activities such as walk and no-walk. In contrast to previous methods for such transitions determination, our solution does not require complex time- or frequency-domain based algorithms. Our solution relies on fusing motion data collected from an inertial measurement unit device with light data generated from visible light sensing technology utilizing an RGB photodiode. By utilizing a low-complex decision tree algorithm, the activity can be precisely recognized in resource-efficient way. The applicability of our approach was tested through two scenarios representing various ADL in smart environment.

The shift toward electric mobility in Germany is a major component of the German climate protection program. In this context, public charging is growing in importance, especially in high-density urban areas, which causes an additional load on the distribution grid. In order to evaluate this impact and prevent possible overloads, realistic models are required. Methods for implementing such models and their application in the context of grid load are research topics that are only minorly addressed in the literature. This paper aims to demonstrate the entire process chain from the selection of a modelling method to the implementation and application of the model within a case study. Applying a stochastic approach, charging points are modelled via probabilities to determine the start of charging, plug-in duration, and charged energy. Subsequently, load profiles are calculated, integrated into an energy system model and applied in order to analyze the effects of a high density of public charging points on the urban distribution grid. The case study highlights a possible application of the implemented probabilistic load profile model, but also reveals its limitations. The primary results of this paper are the identification and evaluation of relevant criteria for modelling the load profiles of public charging points as well as the demonstration of the model and its comparison to real charging processes. By publishing the determined probabilities and the model for calculating the charging load profiles, a comprehensive tool is provided.