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Ballistocardiography (BCG) can be used to monitor heart rate activity. Besides, the accelerometer should have high sensitivity and minimal internal noise; a low-cost approach was taken into consideration. Several measurements have been executed to determine the optimal positioning of a sensor under the mattress to obtain a signal strong enough for further analysis. A prototype for an unobtrusive accelerometer-based measurement system has been developed and tested in a conventional bed without any specific extras. The influence of the human sleep position for the output accelerometer data was tested. The obtained results indicate the potential to capture BCG signals using accelerometers. The measurement system can detect heart rate in an unobtrusive form in the home environment.
Trajectory Tracking of a Fully-actuated Surface Vessel using Nonlinear Model Predictive Control
(2021)
The trajectory tracking problem for a fully-actuated real-scaled surface vessel is addressed in this paper. The unknown hydrodynamic and propulsion parameters of the vessel’s dynamic model were identified using an experimental maneuver-based identification process. Then, a nonlinear model predictive control (NMPC) scheme is designed and the controller’s performance is assessed through the variation of NMPC parameters and constraints tightening for tracking a curved trajectory.
Summary of the 9th workshop on metallization and interconnection for crystalline silicon solar cells
(2021)
The 9th edition of the Workshop on Metallization and Interconnection for Crystalline Silicon Solar Cells was held as an online event but nevertheless reached the workshop goals of knowledge sharing and networking. The technology of screen-printed contacts of high temperature pastes continues its fast progress enabled by better understanding of the phenomena taking place during printing and firing, and progress in materials. Great improvements were also achieved in low temperature paste printing and plated metallization. In the field of interconnection, progress was reported on multiwire approaches, electrically conductive adhesives and on foil-based approaches. Common to many contributions at the workshop was the use of advanced laser processes to improve performance or throughput.
In this paper, a novel measurement model based on spherical double Fourier series (DFS) for estimating the 3D shape of a target concurrently with its kinematic state is introduced. Here, the shape is represented as a star-convex radial function, decomposed as spherical DFS. In comparison to ordinary DFS, spherical DFS do not suffer from ambiguities at the poles. Details will be given in the paper. The shape representation is integrated into a Bayesian state estimator framework via a measurement equation. As range sensors only generate measurements from the target side facing the sensor, the shape representation is modified to enable application of shape symmetries during the estimation process. The model is analyzed in simulations and compared to a shape estimation procedure using spherical harmonics. Finally, shape estimation using spherical and ordinary DFS is compared to analyze the effect of the pole problem in extended object tracking (EOT) scenarios.
Continuous range queries are a common means to handle mobile clients in high-density areas. Most existing approaches focus on settings in which the range queries for location-based services are mostly static whereas the mobile clients in the ranges move. We focus on a category called Dynamic Real-Time Range Queries (DRRQ) assuming that both, clients requested by the query and the inquirers, are mobile. In consequence, the query parameters results continuously change. This leads to two requirements: the ability to deal with an arbitrary high number of mobile nodes (scalability) and the real-time delivery of range query results. In this paper we present the highly decentralized solution Adaptive Quad Streaming (AQS) for the requirements of DRRQs. AQS approximates the query results in favor of a controlled real-time delivery and guaranteed scalability. While prior works commonly optimizes data structures on servers, we use AQS to focus on a highly distributed cell structure without data structures automatically adapting to changing client distributions. Instead of the commonly used request-response approach, we apply a lightweight streaming method in which no bidirectional communication and no storage or maintenance of queries are required at all.
Since its first edition in 2008, the Workshop on Metallization and Interconnection for Crystalline Silicon SolarCells has been a key event where knowledge in the critical fields of crystalline silicon solar cell metallization andinterconnection is shared between experts from academia and industry. It has become a highly recognized event forthe quality of the contributions, the lively Q&A sessions, and the exceptional networking opportunity.The situation with the Covid-19 pandemic made organizing the 9th edition as an in-person event impossible andforced us to reconsider the event format. The event took place virtually on October 5th and 6th 2020. We used aninnovative online platform that enabled not only presentations followed by Q&A but also more informal interactions,where participants could see and talk directly to other participants. 120 experts from 22 countries took part andattended 21 contributions presented live. In spite of a few technical glitches, the workshop was successful and thegoals of exchanging on the state-of-the-art in research/industry and connecting experts in the field were achieved.All presentations are available on www.miworkshop.info as .pdf documents. These proceedings contain asummary of the 9th edition (MIW2020) and peer-reviewed papers based on the workshop contributions. The organizerswish to thank the members of the Scientific Committee for the time spent reviewing the MIW2020 abstracts andproceedings. The organizers also wish to thank again the sponsors and supporters for their financial contributionswhich made the 9th Workshop on Metallization and Interconnection for Crystalline Silicon Solar Cells possible.
Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows
(2021)
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level. However, high fluctuations and increasing electrification cause huge forecast errors with traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus enables various applications in low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein-Polynomial Normalizing Flows where a neural network controls the parameters of the flow. In an empirical study with 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities and also outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.