---
_id: '62643'
author:
- first_name: Tobias
full_name: Schwabe, Tobias
id: '39217'
last_name: Schwabe
- first_name: Christian
full_name: Kress, Christian
id: '13256'
last_name: Kress
orcid: 0000-0002-4403-2237
- first_name: Stephan
full_name: Kruse, Stephan
id: '38254'
last_name: Kruse
- first_name: Maxim
full_name: Weizel, Maxim
id: '44271'
last_name: Weizel
orcid: 0000-0003-2699-9839
- first_name: Hanjo
full_name: Rhee, Hanjo
last_name: Rhee
- first_name: J. Christoph
full_name: Scheytt, J. Christoph
id: '37144'
last_name: Scheytt
orcid: '0000-0002-5950-6618 '
citation:
ama: Schwabe T, Kress C, Kruse S, Weizel M, Rhee H, Scheytt JC. Forward-Biased Silicon
Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation in
a 250 nm EPIC BiCMOS Technology. Journal of Lightwave Technology. 2025;43(1):255-270.
doi:10.1109/JLT.2024.3450949
apa: Schwabe, T., Kress, C., Kruse, S., Weizel, M., Rhee, H., & Scheytt, J.
C. (2025). Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic
Co-Simulation and Validation in a 250 nm EPIC BiCMOS Technology. Journal of
Lightwave Technology, 43(1), 255–270. https://doi.org/10.1109/JLT.2024.3450949
bibtex: '@article{Schwabe_Kress_Kruse_Weizel_Rhee_Scheytt_2025, title={Forward-Biased
Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation
in a 250 nm EPIC BiCMOS Technology}, volume={43}, DOI={10.1109/JLT.2024.3450949},
number={1}, journal={Journal of Lightwave Technology}, author={Schwabe, Tobias
and Kress, Christian and Kruse, Stephan and Weizel, Maxim and Rhee, Hanjo and
Scheytt, J. Christoph}, year={2025}, pages={255–270} }'
chicago: 'Schwabe, Tobias, Christian Kress, Stephan Kruse, Maxim Weizel, Hanjo Rhee,
and J. Christoph Scheytt. “Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic
Co-Simulation and Validation in a 250 Nm EPIC BiCMOS Technology.” Journal of
Lightwave Technology 43, no. 1 (2025): 255–70. https://doi.org/10.1109/JLT.2024.3450949.'
ieee: 'T. Schwabe, C. Kress, S. Kruse, M. Weizel, H. Rhee, and J. C. Scheytt, “Forward-Biased
Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation
in a 250 nm EPIC BiCMOS Technology,” Journal of Lightwave Technology, vol.
43, no. 1, pp. 255–270, 2025, doi: 10.1109/JLT.2024.3450949.'
mla: Schwabe, Tobias, et al. “Forward-Biased Silicon Phase Shifter Modeling for
Electronic-Photonic Co-Simulation and Validation in a 250 Nm EPIC BiCMOS Technology.”
Journal of Lightwave Technology, vol. 43, no. 1, 2025, pp. 255–70, doi:10.1109/JLT.2024.3450949.
short: T. Schwabe, C. Kress, S. Kruse, M. Weizel, H. Rhee, J.C. Scheytt, Journal
of Lightwave Technology 43 (2025) 255–270.
date_created: 2025-11-27T07:14:34Z
date_updated: 2025-11-27T07:16:01Z
department:
- _id: '58'
doi: 10.1109/JLT.2024.3450949
intvolume: ' 43'
issue: '1'
keyword:
- Integrated circuit modeling
- Capacitance
- Silicon
- Modulation
- Adaptation models
- Semiconductor device modeling
- Bandwidth
- Data communication
- electrooptical transmitter
- equalization
- free-carrier-plasma dispersion effect
- modelling
- optical modulator
- phase shifter
- silicon photonics
language:
- iso: eng
page: 255-270
publication: Journal of Lightwave Technology
status: public
title: Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation
and Validation in a 250 nm EPIC BiCMOS Technology
type: journal_article
user_id: '38254'
volume: 43
year: '2025'
...
---
_id: '11813'
abstract:
- lang: eng
text: 'The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising
Autoencoder (DA) both bring performance gains in severe single-channel speech
recognition conditions. The first can be adjusted to different conditions by an
appropriate parameter setting, while the latter needs to be trained on conditions
similar to the ones expected at decoding time, making it vulnerable to a mismatch
between training and test conditions. We use a DNN backend and study reverberant
ASR under three types of mismatch conditions: different room reverberation times,
different speaker to microphone distances and the difference between artificially
reverberated data and the recordings in a reverberant environment. We show that
for these mismatch conditions BFE can provide the targets for a DA. This unsupervised
adaptation provides a performance gain over the direct use of BFE and even enables
to compensate for the mismatch of real and simulated reverberant data.'
author:
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: P.
full_name: Golik, P.
last_name: Golik
- first_name: R.
full_name: Schlueter, R.
last_name: Schlueter
citation:
ama: 'Heymann J, Haeb-Umbach R, Golik P, Schlueter R. Unsupervised adaptation of
a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under
mismatch conditions. In: Acoustics, Speech and Signal Processing (ICASSP),
2015 IEEE International Conference On. ; 2015:5053-5057. doi:10.1109/ICASSP.2015.7178933'
apa: Heymann, J., Haeb-Umbach, R., Golik, P., & Schlueter, R. (2015). Unsupervised
adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant
asr under mismatch conditions. In Acoustics, Speech and Signal Processing (ICASSP),
2015 IEEE International Conference on (pp. 5053–5057). https://doi.org/10.1109/ICASSP.2015.7178933
bibtex: '@inproceedings{Heymann_Haeb-Umbach_Golik_Schlueter_2015, title={Unsupervised
adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant
asr under mismatch conditions}, DOI={10.1109/ICASSP.2015.7178933},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International
Conference on}, author={Heymann, Jahn and Haeb-Umbach, Reinhold and Golik, P.
and Schlueter, R.}, year={2015}, pages={5053–5057} }'
chicago: Heymann, Jahn, Reinhold Haeb-Umbach, P. Golik, and R. Schlueter. “Unsupervised
Adaptation of a Denoising Autoencoder by Bayesian Feature Enhancement for Reverberant
Asr under Mismatch Conditions.” In Acoustics, Speech and Signal Processing
(ICASSP), 2015 IEEE International Conference On, 5053–57, 2015. https://doi.org/10.1109/ICASSP.2015.7178933.
ieee: J. Heymann, R. Haeb-Umbach, P. Golik, and R. Schlueter, “Unsupervised adaptation
of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr
under mismatch conditions,” in Acoustics, Speech and Signal Processing (ICASSP),
2015 IEEE International Conference on, 2015, pp. 5053–5057.
mla: Heymann, Jahn, et al. “Unsupervised Adaptation of a Denoising Autoencoder by
Bayesian Feature Enhancement for Reverberant Asr under Mismatch Conditions.” Acoustics,
Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On,
2015, pp. 5053–57, doi:10.1109/ICASSP.2015.7178933.
short: 'J. Heymann, R. Haeb-Umbach, P. Golik, R. Schlueter, in: Acoustics, Speech
and Signal Processing (ICASSP), 2015 IEEE International Conference On, 2015, pp.
5053–5057.'
date_created: 2019-07-12T05:28:45Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2015.7178933
keyword:
- codecs
- signal denoising
- speech recognition
- Bayesian feature enhancement
- denoising autoencoder
- reverberant ASR
- single-channel speech recognition
- speaker to microphone distances
- unsupervised adaptation
- Adaptation models
- Noise reduction
- Reverberation
- Speech
- Speech recognition
- Training
- deep neuronal networks
- denoising autoencoder
- feature enhancement
- robust speech recognition
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2015/hey_icassp_2015.pdf
oa: '1'
page: 5053-5057
publication: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International
Conference on
status: public
title: Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement
for reverberant asr under mismatch conditions
type: conference
user_id: '44006'
year: '2015'
...
---
_id: '9879'
abstract:
- lang: eng
text: Application of prognostics and health management (PHM) in the field of Proton
Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing
the reliability and availability of these systems. Though a lot of work is currently
being conducted to develop PHM systems for fuel cells, various challenges have
been encountered including the self-healing effect after characterization as well
as accelerated degradation due to dynamic loading, all which make RUL predictions
a difficult task. In this study, a prognostic approach based on adaptive particle
filter algorithm is proposed. The novelty of the proposed method lies in the introduction
of a self-healing factor after each characterization and the adaption of the degradation
model parameters to fit to the changing degradation trend. An ensemble of five
different state models based on weighted mean is then developed. The results show
that the method is effective in estimating the remaining useful life of PEM fuel
cells, with majority of the predictions falling within 5\% error. The method was
employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the
winner of the RUL category of the challenge.
author:
- first_name: 'James Kuria '
full_name: 'Kimotho, James Kuria '
last_name: Kimotho
- first_name: Tobias
full_name: Meyer, Tobias
last_name: Meyer
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter
with model parameter adaptation. In: Prognostics and Health Management (PHM),
2014 IEEE Conference On. ; 2014:1-6. doi:10.1109/ICPHM.2014.7036406'
apa: Kimotho, J. K., Meyer, T., & Sextro, W. (2014). PEM fuel cell prognostics
using particle filter with model parameter adaptation. In Prognostics and Health
Management (PHM), 2014 IEEE Conference on (pp. 1–6). https://doi.org/10.1109/ICPHM.2014.7036406
bibtex: '@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics
using particle filter with model parameter adaptation}, DOI={10.1109/ICPHM.2014.7036406},
booktitle={Prognostics and Health Management (PHM), 2014 IEEE Conference on},
author={Kimotho, James Kuria and Meyer, Tobias and Sextro, Walter}, year={2014},
pages={1–6} }'
chicago: Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell
Prognostics Using Particle Filter with Model Parameter Adaptation.” In Prognostics
and Health Management (PHM), 2014 IEEE Conference On, 1–6, 2014. https://doi.org/10.1109/ICPHM.2014.7036406.
ieee: J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle
filter with model parameter adaptation,” in Prognostics and Health Management
(PHM), 2014 IEEE Conference on, 2014, pp. 1–6.
mla: Kimotho, James Kuria, et al. “PEM Fuel Cell Prognostics Using Particle Filter
with Model Parameter Adaptation.” Prognostics and Health Management (PHM),
2014 IEEE Conference On, 2014, pp. 1–6, doi:10.1109/ICPHM.2014.7036406.
short: 'J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management
(PHM), 2014 IEEE Conference On, 2014, pp. 1–6.'
date_created: 2019-05-20T13:11:02Z
date_updated: 2019-05-20T13:12:27Z
department:
- _id: '151'
doi: 10.1109/ICPHM.2014.7036406
keyword:
- ageing
- particle filtering (numerical methods)
- proton exchange membrane fuel cells
- remaining life assessment
- PEM fuel cell prognostics
- PHM
- RUL predictions
- accelerated degradation
- adaptive particle filter algorithm
- dynamic loading
- model parameter adaptation
- prognostics and health management
- proton exchange membrane fuel cells
- remaining useful life estimation
- self-healing effect
- Adaptation models
- Data models
- Degradation
- Estimation
- Fuel cells
- Mathematical model
- Prognostics and health management
language:
- iso: eng
page: 1-6
publication: Prognostics and Health Management (PHM), 2014 IEEE Conference on
status: public
title: PEM fuel cell prognostics using particle filter with model parameter adaptation
type: conference
user_id: '55222'
year: '2014'
...