Mitarbeiter

M. Sc. Jonas Fuchs

Kontakt

  • E-Mail:
  • Telefon: 09131/85-27190
  • Fax: 09131/85-28730
  • Raum: 04.236
  • Cauerstraße 9
    91058 Erlangen

Über Jonas Fuchs

Lebenslauf

Jonas Fuchs schloss sein Bachelorstudium im Fach Elektrotechnik-Elektronik-Informationstechnik (EEI) an der Friedrich-Alexander-Universität Erlangen-Nürnberg im April 2016 ab. Das anschließende Masterstudium im Fach EEI mit dem Schwerpunkt Informationstechnik beendete er im Februar 2018 erfolgreich. Seit Juni 2018 ist er als wissenschaftlicher Mitarbeiter im Team Circuits, Systems und Hardware Test (CST) am Lehrstuhl für Technische Elektronik beschäftigt.

Arbeitsgebiete

  • Digitale Radar-Signalverarbeitung und Machine-Learning Algorithmen

Abschlussarbeiten

Abschlussarbeiten StudOn

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Preise & Auszeichnungen

  • J. Fuchs, ARGUS Award 2018, HENSOLDT Sensors GmbH, 2018. [Bibtex]
    @prize{fuchs_prize_2018,
    abstract = {Masterarbeit: Implementierung und Evaluierung von Verfahren zur Winkelschätzung mittels Neuronaler Netze für FMCW-Radarsysteme},
    author = {Fuchs, Jonas},
    booktitle = {HENSOLDT Sensors GmbH},
    cris = {fuchs_prize_2018},
    year = {2018},
    title = {ARGUS Award 2018},
    type = {20773-Kleiner Preis},
    }
  • J. Fuchs, SEMIKRON Student Award, SEMIKRON International GmbH, 2013. [Bibtex]
    @prize{fuchs_prize_2013,
    author = {Fuchs, Jonas},
    booktitle = {SEMIKRON International GmbH},
    cris = {fuchs_prize_2013},
    year = {2013},
    month = {07},
    day = {12},
    title = {SEMIKRON Student Award},
    type = {20773-Kleiner Preis},
    }

COPYRIGHT NOTICE: Copyright and all rights of the material above are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by the appropriate copyright. The material may not be reposted without the explicit permission of the copyright holder.

COPYRIGHT NOTICE FOR IEEE PUBLICATIONS: © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

COPYRIGHT NOTICE FOR EUMA PUBLICATIONS: © EUMA. Personal use of this material is permitted. Permission from European Microwave Association(EUMA) must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publikationen

2020

  • M. Lübke, J. Fuchs, V. Shatov, A. Dubey, R. Weigel, and F. Lurz, "Simulation Environment of a Communication System Using CDMA at 77 GHz" in Wireless Communications & Mobile Computing (IWCMC 2020), Limassol, Cyprus, 2020 (to be published). [Bibtex]
    @inproceedings{luebke2020a,
    abstract = {In this paper, an overall concept for a joint communication-sensing system at 77 GHz is presented with special focus on the communication part.To take advantage of the reduced interference between vehicles, code division multiplexing using direct sequence spread spectrum signals is applied. The system, which covers the whole signal processing chain, is introduced, explained and simulated using Simulink/Matlab software. A design, capable of spreading, modulating, including non-idealities of radio frequency-blocks and synchronization, is built up and evaluated. Additionally, a channel model is simulated in the software WinProp and integrated in the Simulink simulation. In consequence, a more realistic model compared to an estimation-based Rician-or Rayleigh channel model is realized. Furthermore, different modulation schemes, binary phase shift keying, 4- and 16-quadrature amplitude modulation, are investigated. The system is rated with respect to the bit-error-rate by applying additive white Gaussian noise. The functionality is verified as the the results match with the theoretical assumptions The system is further improved by building up a Rake-Receiver structure.},
    author = {Lübke, Maximilian and Fuchs, Jonas and Shatov, Victor and Dubey, Anand and Weigel, Robert and Lurz, Fabian},
    booktitle = {Wireless Communications & Mobile Computing (IWCMC 2020)},
    cris = {https://cris.fau.de/converis/publicweb/publication/236628043},
    year = {2020},
    month = {06},
    day = {15},
    eventdate = {2020-06-15/2020-06-19},
    faupublication = {yes},
    keywords = {77 GHz; joint sensing-communications; direct spread spectrum communication; rake demodulator},
    note = {unpublished},
    peerreviewed = {automatic},
    title = {Simulation Environment of a Communication System Using CDMA at 77 GHz},
    venue = {Limassol, Cyprus},
    }
  • M. Gardill, J. Schwendner, and J. Fuchs, "An Approach to Over-the-air Synchronization of Commercial Chirp-Sequence Automotive Radar Sensors" in 2020 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT), San Antonio, Texas, USA, 2020, pp. 1-4. [Bibtex]
    @inproceedings{gardill2020,
    abstract = {An approach to wireless synchronization between two commercial 77GHz automotive FMCW radar sensors is shown. Our focus is the alignment of a passive listening sensor to a second active transmitting sensor in frequency, time, and waveform modulation parameters, just by observing the signals transmitted from the active sensor. We show that using a combination of inter- and intra-chirpsequence synchronization, the active sensor’s signal can be successfully de-ramped for fast-chirp-sequence waveforms with 275MHz of bandwidth. In addition, we discuss timing requirements and challenges, and characterize the remaining synchronization errors by an analysis of the IF signal data matrix of the de-ramped waveform.},
    author = {Gardill, Markus and Schwendner, Johannes and Fuchs, Jonas},
    language = {English},
    booktitle = {2020 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT)},
    cris = {https://cris.fau.de/converis/publicweb/publication/229092398},
    year = {2020},
    month = {05},
    day = {26},
    eventdate = {2020-01-26/2020-01-29},
    faupublication = {yes},
    pages = {1--4},
    peerreviewed = {unknown},
    title = {An Approach to Over-the-air Synchronization of Commercial Chirp-Sequence Automotive Radar Sensors},
    type = {Konferenzschrift},
    venue = {San Antonio, Texas, USA},
    }
  • A. Dubey, J. Fuchs, M. Lübke, R. Weigel, and F. Lurz, "Generative Adversial Network based Extended Target Detection for Automotive MIMO Radar" in 2018 International Conference on Radar (RADAR), Washington DC, USA, 2020 (to be published). [Bibtex]
    @inproceedings{dubey2020,
    abstract = {In recent years, the automotive radar systems has gained substantial interest for different applications of autonomous driving. The performance of most applications likes classification and tracking directly relies on accurate target detection. The state-of-the-art detection pipeline is vulnerable to multi-path reflections, clutter noise, interference from another radar and leads to false or ghost detections. To address this issue, an end-to-end target detection pipeline using a residual based U-Net architecture is proposed. In contrast to the conventional approach, the network directly generates the detection map from range-Doppler map. The network uses a generative adversarial training over multiple real world measurements. We demonstrate that the proposed network can learn effectively to detect extended targets and shows significant improvement under increased noise floor in comparison to the state-of-the-art detection techniques.},
    author = {Dubey, Anand and Fuchs, Jonas and Lübke, Maximilian and Weigel, Robert and Lurz, Fabian},
    language = {English},
    booktitle = {2018 International Conference on Radar (RADAR)},
    cris = {https://cris.fau.de/converis/publicweb/publication/231325915},
    year = {2020},
    month = {04},
    day = {27},
    eventdate = {2020-04-27/2020-05-01},
    faupublication = {yes},
    note = {unpublished},
    peerreviewed = {automatic},
    title = {Generative Adversial Network based Extended Target Detection for Automotive MIMO Radar},
    venue = {Washington DC, USA},
    }
  • J. Fuchs, A. Dubey, M. Lübke, R. Weigel, and F. Lurz, "Automotive Radar Interference Mitigation using a Convolutional Autoencoder" in 2020 International Conference on Radar (RADAR), Washington DC, USA, 2020 (to be published). [Bibtex]
    @inproceedings{fuchs2020,
    abstract = {Automotive radar interference imposes big challenges on signal processing algorithms as it raises the noise floor and consequently lowers the detection probability. With limited frequency bands and increasing number of sensors per car, avoidance techniques such as frequency hopping or beamforming quickly become insufficient. Detect-and-repair strategies have been studied intensively for the automotive field, to reconstruct the affected signal samples. However depending on the type of interference, reconstruction of the time domain signals is a highly non-trivial task, which can affect following signal processing modules. In this work an autoencoder based convolutional neural network is proposed to perform image based denoising. Interference mitigation is phrased as a denoising task directly on the range-Doppler spectrum. The neural networks shows significant improvement with respect to signal-to-noise-plus-interference ratio in comparison to other state-of-the-art mitigation techniques, while better preserving phase information of the spectrum compared to other techniques.},
    author = {Fuchs, Jonas and Dubey, Anand and Lübke, Maximilian and Weigel, Robert and Lurz, Fabian},
    language = {English},
    booktitle = {2020 International Conference on Radar (RADAR)},
    cris = {https://cris.fau.de/converis/publicweb/publication/231966704},
    year = {2020},
    month = {04},
    day = {27},
    eventdate = {2020-04-27/2020-05-01},
    faupublication = {yes},
    note = {unpublished},
    peerreviewed = {unknown},
    title = {Automotive Radar Interference Mitigation using a Convolutional Autoencoder},
    type = {Konferenzschrift},
    venue = {Washington DC, USA},
    }
  • M. Lübke, J. Fuchs, V. Shatov, A. Dubey, R. Weigel, and F. Lurz, "Combining Radar and Communication at 77 GHz Using a CDMA Technique" in 2020 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Linz, Austria, 2020 (to be published). [Bibtex]
    @inproceedings{luebke2020,
    author = {Lübke, Maximilian and Fuchs, Jonas and Shatov, Victor and Dubey, Anand and Weigel, Robert and Lurz, Fabian},
    booktitle = {2020 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)},
    cris = {https://cris.fau.de/converis/publicweb/publication/234501926},
    year = {2020},
    month = {04},
    day = {20},
    eventdate = {2020-04-20/2020-04-22},
    faupublication = {yes},
    note = {unpublished},
    peerreviewed = {automatic},
    title = {Combining Radar and Communication at 77 GHz Using a CDMA Technique},
    venue = {Linz, Austria},
    }
  • K. Shi, S. Schellenberger, C. Will, T. Steigleder, F. Michler, J. Fuchs, R. Weigel, C. Ostgathe, and A. Koelpin, "A dataset of radar-recorded heart sounds and vital signs including synchronised reference sensor signals", Scientific Data, 2020. [Bibtex]
    @article{shi2020,
    abstract = {Radar systems allow for contactless measurements of vital signs such as heart sounds, the pulse signal, and respiration. This approach is able to tackle crucial disadvantages of state-of-the-art monitoring devices such as the need for permanent wiring and skin contact. Potential applications include the employment in a hospital environment but also in home care or passenger vehicles. This dataset consists of synchronised data which are acquired using a Six-Port-based radar system operating at 24GHz, a digital stethoscope, an ECG, and a respiration sensor. 11 test subjects were measured in different defined scenarios and at several measurement positions such as at the carotid, the back, and several frontal positions on the thorax. Overall, around 223 minutes of data were acquired at scenarios such as breath-holding, post-exercise measurements, and while speaking. The presented dataset contains reference-labeled ECG signals and can therefore easily be used to either test algorithms for monitoring the heart rate, but also to gain insights about characteristic effects of radar-based vital sign monitoring.
    }, author = {Shi, Kilin and Schellenberger, Sven and Will, Christoph and Steigleder, Tobias and Michler, Fabian and Fuchs, Jonas and Weigel, Robert and Ostgathe, Christoph and Koelpin, Alexander}, language = {English}, publisher = {Springer Nature}, cris = {https://cris.fau.de/converis/publicweb/publication/232936220}, year = {2020}, month = {02}, day = {13}, faupublication = {yes}, issn = {2052-4463}, journaltitle = {Scientific Data}, peerreviewed = {Yes}, title = {A dataset of radar-recorded heart sounds and vital signs including synchronised reference sensor signals}, type = {Article in Journal}, }
  • A. Dubey, J. Fuchs, T. Reißland, R. Weigel, and F. Lurz, "Uncertainty Analysis of Deep Neural Network for Classification of Vulnerable Road Users using micro-Doppler" in IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet), San Antonio, Texas, USA, 2020 (to be published). [Bibtex]
    @inproceedings{dubey2020a,
    abstract = {Unlike optical imaging, it’s difficult to extract descriptive features from radar data for problems like classification of different targets. This paper takes the advantage of different neural network based architectures such as convolutional neural networks and long-short term memory to propose an end-to-end framework for classification of vulnerable road users. To make the network’s prediction more reliable for automotive applications, a new concept of network uncertainty is introduced to the defined architectures. The signal processing tool chain described in this paper achieves higher accuracy than state-of-the-art algorithms while maintaining latency requirement for automotive applications.},
    author = {Dubey, Anand and Fuchs, Jonas and Reißland, Torsten and Weigel, Robert and Lurz, Fabian},
    language = {English},
    booktitle = {IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet)},
    cris = {https://cris.fau.de/converis/publicweb/publication/229613648},
    year = {2020},
    month = {01},
    day = {26},
    eventdate = {2020-01-26/2020-01-29},
    faupublication = {yes},
    keywords = {Autonomous Driving,Automotive Radar,Deep Neural Networks,Mico-Doppler,VRU Classification.},
    note = {unpublished},
    peerreviewed = {automatic},
    title = {Uncertainty Analysis of Deep Neural Network for Classification of Vulnerable Road Users using micro-Doppler},
    venue = {San Antonio, Texas, USA},
    }

2019

  • J. Fuchs, R. Weigel, and M. Gardill, "Single-Snapshot Direction-of-Arrival Estimation of Multiple Targets using a Multi-Layer Perceptron" in 2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Detroit, MI, USA, 2019, pp. 1-4. [DOI] [Bibtex]
    @inproceedings{fuchs2019a,
    abstract = {An alternative approach to high-resolution direction-of-arrival estimation in the context of automotive FMCW signal processing is shown by training a neural network with simulation as well as experimental data to estimate the mean and distance of the azimuth angles from two targets. Testing results are post-processed to obtain the estimated azimuth angles which can be validated afterwards. The performance of the proposed neural network is then compared with a reference implementation of a maximum likelihood estimator. Final evaluations show super-resolution like performance with significantly reduced computation time, which is expected to have an impact on future multi-dimensional high-resolution DoA estimation.},
    author = {Fuchs, Jonas and Weigel, Robert and Gardill, Markus},
    language = {English},
    booktitle = {2019 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)},
    cris = {https://cris.fau.de/converis/publicweb/publication/211247235},
    year = {2019},
    month = {06},
    day = {03},
    doi = {10.1109/ICMIM.2019.8726554},
    eventdate = {2019-04-15/2019-04-16},
    faupublication = {yes},
    keywords = {Direction-of-arrival estimation;Training;Neural networks;Radar;Maximum likelihood estimation;Signal to noise ratio;Automotive Radar;Direction-of-Arrival Estimation;Neural Network;Multi-Layer Perceptron},
    pages = {1--4},
    peerreviewed = {unknown},
    title = {Single-Snapshot Direction-of-Arrival Estimation of Multiple Targets using a Multi-Layer Perceptron},
    type = {Konferenzschrift},
    venue = {Detroit, MI, USA},
    }
  • M. Gardill, J. Schwendner, and J. Fuchs, "In-Situ Time-Frequency Analysis of the 77 GHz Bands using a Commercial Chirp-Sequence Automotive FMCW Radar Sensor" in 2019 IEEE MTT-S International Microwave Symposium (IMS), Boston, MA, USA, 2019, pp. 544-547. [Bibtex]
    @inproceedings{gardill2019,
    abstract = {A commercial chirp-sequence automotive radar sensor is reconfigured to allow for time-frequency analysis of the 77GHz automotive radar bands. Compared to highend measurement equipment such as real-time spectrum analyzers, due to its low cost and compact size the proposed approach allows for in-situ interception and time-frequency analysis of traffic scenarios, e.g. by mounting the sensor in a test vehicle exactly at the positions where the radar sensors would be mounted. The theory behind the modification is discussed and compared with measurements. Time-frequency spectra of a rural road driving scenario obtained with the proposed system are shown to illustrate the practical relevance and usefulness of the approach.},
    author = {Gardill, Markus and Schwendner, Johannes and Fuchs, Jonas},
    language = {English},
    booktitle = {2019 IEEE MTT-S International Microwave Symposium (IMS)},
    cris = {https://cris.fau.de/converis/publicweb/publication/210771629},
    year = {2019},
    month = {05},
    day = {07},
    eventdate = {2019-06-02/2019-02-07},
    faupublication = {yes},
    keywords = {radar; automotive; FMCW; chirp-sequence; time-frequency analysis},
    pages = {544--547},
    peerreviewed = {unknown},
    title = {In-Situ Time-Frequency Analysis of the 77 GHz Bands using a Commercial Chirp-Sequence Automotive FMCW Radar Sensor},
    type = {Konferenzschrift},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8700983&isnumber=8700641},
    venue = {Boston, MA, USA},
    }
  • J. Fuchs, R. Weigel, and M. Gardill, "Model Order Estimation using a Multi-Layer Perceptron for Direction-of-Arrival Estimation in Automotive Radar Sensors" in 2019 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet), Orlando, Florida, USA, 2019, pp. 1-3. [DOI] [Bibtex]
    @inproceedings{fuchs2019,
    abstract = {In this work, a machine-learning-based approach to decide whether one or two targets are present in the same range-velocity cell of a chirp-sequence FMCW radar system is evaluated. An experimental setup for generating sufficient large sets of training and testing data using real measurement data from automotive 77 GHz radar sensors is presented. Using this data a multi-layer perceptron is trained to directly estimate the number of present targets from the received signals in order to determine if resolution in the spatial domain is necessary. Evaluations of the trained model show that the network is able to inherently learn the underlying signal model and reach super-resolution performance.
    }, author = {Fuchs, Jonas and Weigel, Robert and Gardill, Markus}, language = {English}, booktitle = {2019 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNet)}, cris = {https://cris.fau.de/converis/publicweb/publication/203918515}, year = {2019}, month = {01}, day = {20}, doi = {10.1109/WISNET.2019.8711806}, eventdate = {2019-01-20/2019-01-23}, faupublication = {yes}, keywords = {Automotive Radar; Direction-of-Arrival Estimation; Neural Network; Multi-Layer Perceptron}, pages = {1--3}, peerreviewed = {unknown}, title = {Model Order Estimation using a Multi-Layer Perceptron for Direction-of-Arrival Estimation in Automotive Radar Sensors}, type = {Konferenzschrift}, venue = {Orlando, Florida, USA}, }

2018

  • M. Gardill, J. Fuchs, C. Frank, and R. Weigel, "A Multi-Layer Perceptron Applied to Number of Target Indication for Direction-of-Arrival Estimation in Automotive Radar Sensors" in 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), Aalborg, Denmark, 2018, pp. 1-6. [DOI] [Bibtex]
    @inproceedings{gardill2018,
    author = {Gardill, Markus and Fuchs, Jonas and Frank, Christian and Weigel, Robert},
    language = {English},
    booktitle = {2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)},
    cris = {https://cris.fau.de/converis/publicweb/publication/200736205},
    year = {2018},
    month = {10},
    day = {17},
    doi = {10.1109/MLSP.2018.8516952},
    eventdate = {2018-09-17/2018-09-20},
    faupublication = {yes},
    keywords = {Automotive Radar; Direction-of-Arrival Estimation; Neural Network; Multi-Layer Perceptron},
    pages = {1--6},
    peerreviewed = {Yes},
    title = {A Multi-Layer Perceptron Applied to Number of Target Indication for Direction-of-Arrival Estimation in Automotive Radar Sensors},
    type = {Konferenzschrift},
    venue = {Aalborg, Denmark},
    }
  • C. Will, K. Shi, S. Schellenberger, T. Steigleder, F. Michler, J. Fuchs, R. Weigel, C. Ostgathe, and A. Koelpin, "Radar-Based Heart Sound Detection", Scientific Reports, 2018. [DOI] [Bibtex]
    @article{will2018,
    abstract = {This paper introduces heart sound detection by radar systems, which enables touch-free and continuous monitoring of heart sounds. The proposed measurement principle entails two enhancements in modern vital sign monitoring. First, common touch-based auscultation with a phonocardiograph can be simplified by using biomedical radar systems. Second, detecting heart sounds offers a further feasibility in radar-based heartbeat monitoring. To analyse the performance of the proposed measurement principle, 9930 seconds of eleven persons-under-tests' vital signs were acquired and stored in a database using multiple, synchronised sensors: a continuous wave radar system, a phonocardiograph (PCG), an electrocardiograph (ECG), and a temperature-based respiration sensor. A hidden semi-Markov model is utilised to detect the heart sounds in the phonocardiograph and radar data and additionally, an advanced template matching (ATM) algorithm is used for state-of-the-art radar-based heartbeat detection. The feasibility of the proposed measurement principle is shown by a morphology analysis between the data acquired by radar and PCG for the dominant heart sounds S1 and S2: The correlation is 82.97 ± 11.15% for 5274 used occurrences of S1 and 80.72 ± 12.16% for 5277 used occurrences of S2. The performance of the proposed detection method is evaluated by comparing the F-scores for radar and PCG-based heart sound detection with ECG as reference: Achieving an F1 value of 92.22 ± 2.07%, the radar system approximates the score of 94.15 ± 1.61% for the PCG. The accuracy regarding the detection timing of heartbeat occurrences is analysed by means of the root-mean-square error: In comparison to the ATM algorithm (144.9 ms) and the PCG-based variant (59.4 ms), the proposed method has the lowest error value (44.2 ms). Based on these results, utilising the detected heart sounds considerably improves radar-based heartbeat monitoring, while the achieved performance is also competitive to phonocardiography.
    }, author = {Will, Christoph and Shi, Kilin and Schellenberger, Sven and Steigleder, Tobias and Michler, Fabian and Fuchs, Jonas and Weigel, Robert and Ostgathe, Christoph and Koelpin, Alexander}, cris = {https://cris.fau.de/converis/publicweb/publication/202373734}, year = {2018}, month = {07}, day = {26}, doi = {10.1038/S41598-018-29984-5}, faupublication = {yes}, issn = {2045-2322}, journaltitle = {Scientific Reports}, peerreviewed = {Yes}, title = {Radar-Based Heart Sound Detection}, type = {online publication}, url = {https://www.nature.com/articles/s41598-018-29984-5}, }

2017

  • C. Will, M. Sporer, N. Sebald, J. Fuchs, R. Weigel, and A. Koelpin, "Radarbasiertes Structural Health Monitoring der Rotorblätter von Windkaftanlagen" in Kleinheubacher Tagung, Miltenberg, Germany, 2017. [Bibtex]
    @inproceedings{will2017b,
    author = {Will, Christoph and Sporer, Michael and Sebald, Nina and Fuchs, Jonas and Weigel, Robert and Koelpin, Alexander},
    publisher = {URSI},
    booktitle = {Kleinheubacher Tagung},
    cris = {https://cris.fau.de/converis/publicweb/publication/123490664},
    year = {2017},
    month = {09},
    day = {25},
    eventdate = {2017-09-25/2017-09-27},
    faupublication = {yes},
    peerreviewed = {Yes},
    title = {Radarbasiertes Structural Health Monitoring der Rotorblätter von Windkaftanlagen},
    type = {Abstract zum Vortrag},
    venue = {Miltenberg, Germany},
    }

COPYRIGHT NOTICE: Copyright and all rights of the material above are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by the appropriate copyright. The material may not be reposted without the explicit permission of the copyright holder.

COPYRIGHT NOTICE FOR IEEE PUBLICATIONS: © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

COPYRIGHT NOTICE FOR EUMA PUBLICATIONS: © EUMA. Personal use of this material is permitted. Permission from European Microwave Association(EUMA) must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.