@article{PogorzelskiHorsthemkeHomrighausenetal.2024, author = {Pogorzelski, Jens and Horsthemke, Ludwig and Homrighausen, Jonas and Stiegek{\"o}tter, Dennis and Gregor, Markus and Gl{\"o}sek{\"o}tter, Peter}, title = {Compact and Fully Integrated LED Quantum Sensor Based on NV Centers in Diamond}, series = {Compact and Fully Integrated LED Quantum Sensor Based on NV Centers in Diamond}, volume = {2024}, journal = {Compact and Fully Integrated LED Quantum Sensor Based on NV Centers in Diamond}, number = {24(3)}, doi = {10.25974/fhms-17569}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:836-opus-175692}, year = {2024}, abstract = {Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in diamond nano or microcrystals is a promising technology for sensitive, integrated magnetic-field sensors. Currently, this technology is still cost-intensive and mainly found in research. Here we propose one of the smallest fully integrated quantum sensors to date based on nitrogen vacancy (NV) centers in diamond microcrystals. It is an extremely cost-effective device that integrates a pump light source, photodiode, microwave antenna, filtering and fluorescence detection. Thus, the sensor offers an all-electric interface without the need to adjust or connect optical components. A sensitivity of 28.32nT/Hz---√ and a theoretical shot noise limited sensitivity of 2.87 nT/Hz---√ is reached. Since only generally available parts were used, the sensor can be easily produced in a small series. The form factor of (6.9 × 3.9 × 15.9) mm3 combined with the integration level is the smallest fully integrated NV-based sensor proposed so far. With a power consumption of around 0.1W, this sensor becomes interesting for a wide range of stationary and handheld systems. This development paves the way for the wide usage of quantum magnetometers in non-laboratory environments and technical applications.}, language = {de} } @article{LoechteRojasRuizGloesekoetter2021, author = {L{\"o}chte, Andre and Rojas Ruiz, Ignacio and Gl{\"o}sek{\"o}tter, Peter}, title = {Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents}, series = {Applied Sciences}, volume = {12}, journal = {Applied Sciences}, number = {1}, isbn = {978-84-1117-173-1}, doi = {10.3390/app12010274}, pages = {275}, year = {2021}, abstract = {The demand for energy storage is increasing massively due to the electrification of transport and the expansion of renewable energies. Current battery technologies cannot satisfy this growing demand as they are difficult to recycle, as the necessary raw materials are mined under precarious conditions, and as the energy density is insufficient. Metal-air batteries offer a high energy density as there is only one active mass inside the cell and the cathodic reaction uses the ambient air. Various metals can be used, but zinc is very promising due to its disposability and non-toxic behavior, and as operation as a secondary cell is possible. Typical characteristics of zinc-air batteries are flat charge and discharge curves. On the one hand, this is an advantage for the subsequent power electronics, which can be optimized for smaller and constant voltage ranges. On the other hand, the state determination of the system becomes more complex, as the voltage level is not sufficient to determine the state of the battery. In this context, electrochemical impedance spectroscopy is a promising candidate as the resulting impedance spectra depend on the state of charge, working point, state of aging, and temperature. Previous approaches require a fixed operating state of the cell while impedance measurements are being performed. In this publication, electrochemical impedance spectroscopy is therefore combined with various machine learning techniques to also determine successfully the state of charge during charging of the cell at non-fixed charging currents. Keywords: electrochemical impedance spectroscopy; artificial neural networks; support vector regression; zinc-air battery; state estimation; state of charge}, language = {en} } @article{HomrighausenHorsthemkePogorzelskietal.2023, author = {Homrighausen, Jonas and Horsthemke, Ludwig and Pogorzelski, Jens and Trinschek, Sarah and Gl{\"o}sek{\"o}tter, Peter and Gregor, Markus}, title = {Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond}, series = {Sensors}, volume = {23}, journal = {Sensors}, number = {3}, doi = {10.3390/s23031119}, year = {2023}, abstract = {Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.}, language = {en} } @inproceedings{LoechteThranowWintersetal.2022, author = {L{\"o}chte, Andre and Thranow, Jan-Ole and Winters, Felix and Gl{\"o}sek{\"o}tter, Peter}, title = {Analysis of switching electronics for metal-air batterie}, series = {ICECET, Prag}, booktitle = {ICECET, Prag}, doi = {10.1109/ICECET55527.2022.9872910}, year = {2022}, abstract = {The subject of this paper is the analysis of various switching electronics for batteries with separate electrodes for charging and discharging. The aim is to find a switching method that is energy-efficient on the one hand, but also economically viable on the other. Both relays and MOSFETs are suitable for switching between the electrodes. Both variants have advantages and disadvantages. The results show that a solution with MOSFETs is generally more energy-efficient, but requires a large number of cycles to be economically viable compared to the relay.}, language = {de} } @inproceedings{RoerupGloesekoetterPomaresetal.2021, author = {R{\"o}rup, Tim and Gl{\"o}sek{\"o}tter, Peter and Pomares, Hector and Ruiz, Ignacio}, title = {Deep Learning Based Neural Network for Six-Class-Classification of Alzheimer's Disease Stages Based on MRI Images}, series = {IWANN2021}, booktitle = {IWANN2021}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-030-85030-2_1}, pages = {3 -- 14}, year = {2021}, abstract = {State of the art classifiers split Alzheimer's disease progression into a limited number of stages and use a comparatively small database. For the best treatment, it is desirable to have the highest resolution from the progression of the disease. This paper proposes a reliable deep convolutional neural network for the classification of six different Alzheimer's disease stages based on Magnetic Resonance Imaging (MRI). The peculiarity of this paper is the introduction of a new, sixth, disease stage, and the large amount of data that has been taken into account. Additionally, not only the testing accuracy is analyzed, but also the robustness of the classifier to have feedback on how certain the neural network makes its predictions.}, language = {de} } @inproceedings{GomezZuluagaOrdikhaniBaueretal.2021, author = {G{\´o}mez Zuluaga, Mauricio Andr{\´e}s and Ordikhani, Ahmad and Bauer, Christoph and Gl{\"o}sek{\"o}tter, Peter}, title = {Development and implementation of a neural network-based abnormal state prediction system for a piston pump}, series = {IWANN2021}, booktitle = {IWANN2021}, publisher = {Springer Nature Switzerland AG 2021}, isbn = {978-3-030-85029-6}, doi = {10.1007/978-3-030-85030-2_24}, year = {2021}, abstract = {Piston pumps play a key role in factory automation and their availability is very critical for the smooth running of production processes. Modern installations, such as production plants and machines, are becoming increasingly complex. Therefore, the probability of a complete system failure due to a single critical component also increases. Maintenance processes with intelligent devices are therefore very important to achieve maximum economic efficiency and safety. Periodic or continuous monitoring of system components provides key information about the current physical state of the system, enabling early detection of emerging failures. Knowledge of future failures makes it possible to move from the concept of preventive maintenance to intelligent predictive maintenance. In this way, consequential damage and complete system failure can be avoided, maximizing system availability and safety. This paper reflects the development and implementation of a neural network system for abnormal state prediction of piston pumps. After a short introduction into piston pumps and their potential abnormal states, statistical and periodical analysis are presented. Then the design and implementation of suitable neural networks are discussed. Finally, a conclusion is drawn and the observed accuracies as well as potential next steps are discussed.}, language = {de} } @inproceedings{WansingBanosPomaresetal.2015, author = {Wansing, Christian and Banos, Oresti and Pomares, Hector and Gl{\"o}sek{\"o}tter, Peter and Rojas Ruiz, Ignacio}, title = {Development of a platform for the exchange of biodatasets with integrated opportunities for artificial intelligence using MatLab}, editor = {Junta de Andalucia Project P12-TIC-2082., Conference Paper}, doi = {10.1109/ICoCS.2015.7483278}, pages = {1 -- 6}, year = {2015}, abstract = {This paper deals with the issue of automating the process of machine learning and analyzing bio-datasets. For this a user-friendly website has been developed for the interaction with the researchers. On this website it is possible to upload datasets and to share them, if desired, with other scientists. The uploaded data can also be analyzed by various methods and functions. The signals inside these datasets can also be visualized. Furthermore several algorithms have been implemented to create machine learning models with the uploaded data. Based on these generated models new data can be classified or calculated. For all these applications the simplest possible handling was implemented to make the website available to all interested researchers.}, language = {de} } @inproceedings{HorsthemkeBischoffGloesekoetteretal.2020, author = {Horsthemke, Ludwig and Bischoff, Christian and Gl{\"o}sek{\"o}tter, Peter and Burchard, Bernd and Staacke, Robert and Meijer, Jan}, title = {All optical readout scheme for photoluminescence based magnetic field sensors}, series = {2020 IEEE Sensors, Rotterdam, Netherlands}, booktitle = {2020 IEEE Sensors, Rotterdam, Netherlands}, doi = {10.1109/SENSORS47125.2020.9278923}, pages = {1 -- 3}, year = {2020}, abstract = {An improvement on a concept for all optical mag- netometry using nitrogen vacancies in diamond is presented. The concept is based on the fluorescence attenuation of optically pumped nitrogen vacancies by magnetic fields up to ≈ 50 mT. The attenuation is registered by modulating the pumping power to generate a constant signal at a photodetector. A sensitivity of 2.6μT/√Hz at a sampling frequency of 500 Hz is achieved.}, language = {de} } @article{LoechteThranowGebingetal.2020, author = {L{\"o}chte, Andre and Thranow, Jan-Ole and Gebing, Marcel and Horsthemke, Ludwig and Gl{\"o}sek{\"o}tter, Peter}, title = {Forschungsprojekt Zink-Luft-Akkumulator an der FH M{\"u}nster}, series = {VDI Ingenieur forum}, volume = {H 45620}, journal = {VDI Ingenieur forum}, number = {2/2020}, pages = {50 -- 51}, year = {2020}, abstract = {Die wachsenden Anteile fluktuierender rege­nerativer Energien in der Energieversorgung (bis 2020 sollen 30 \% und 2050 sogar So \% des Stroms aus regenerativen Energiequellen stammen) sowie die Steigerung der Elektro­mobilit{\"a}t machen deutlich: Das Thema der Zwischenspeicherung elektrischer Energie ist von h{\"o}chster gesellschaftlicher Relevanz und verlangt zwingend nach einer L{\"o}sung. Neue Technologien, die umweltfreundlich, sicher, leistungsf{\"a}hig und bezahlbar zugleich sind, m{\"u}ssen deshalb entwickelt werden.}, language = {de} } @inproceedings{HorsthemkeStaakeBurchardetal.2020, author = {Horsthemke, Ludwig and Staake, Robert and Burchard, Bernd and Meijer, Jan and Bischoff, Christian and Gl{\"o}sek{\"o}tter, Peter}, title = {Highly Sensitive Compact Room Temperature Quantum Scalar Magnetormeter}, series = {SMSI 2020}, booktitle = {SMSI 2020}, doi = {10.5162/SMSI2020/A1.4}, pages = {47 -- 48}, year = {2020}, abstract = {Magnetometry with nitrogen-vacancy (NV) defects in diamond has been extensively stud-ied in the past [1]. While most approaches in-clude the use of microwaves (MW) for the de-tection of electron spin resonance, only few investigate the sensitivity of the photolumines-cence (PL) from NV centers to an external magnetic field without MW [2, 3, 4]. This work aims to utilize this effect to build a highly sensi-tive and compact room temperature magne-tometer. The avoidance of MW serves the re-duction of production costs and allows a com-mercialization at the current patent situation.}, language = {de} }