[{"language":[{"iso":"eng"}],"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"],"user_id":"38254","department":[{"_id":"58"}],"_id":"62643","status":"public","type":"journal_article","publication":"Journal of Lightwave Technology","doi":"10.1109/JLT.2024.3450949","title":"Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation in a 250 nm EPIC BiCMOS Technology","date_created":"2025-11-27T07:14:34Z","author":[{"id":"39217","full_name":"Schwabe, Tobias","last_name":"Schwabe","first_name":"Tobias"},{"first_name":"Christian","full_name":"Kress, Christian","id":"13256","last_name":"Kress","orcid":"0000-0002-4403-2237"},{"first_name":"Stephan","last_name":"Kruse","full_name":"Kruse, Stephan","id":"38254"},{"first_name":"Maxim","last_name":"Weizel","orcid":"0000-0003-2699-9839","full_name":"Weizel, Maxim","id":"44271"},{"first_name":"Hanjo","full_name":"Rhee, Hanjo","last_name":"Rhee"},{"first_name":"J. Christoph","last_name":"Scheytt","orcid":"0000-0002-5950-6618 ","id":"37144","full_name":"Scheytt, J. Christoph"}],"volume":43,"date_updated":"2025-11-27T07:16:01Z","citation":{"apa":"Schwabe, T., Kress, C., Kruse, S., Weizel, M., Rhee, H., &#38; Scheytt, J. C. (2025). Forward-Biased Silicon Phase Shifter Modeling for Electronic-Photonic Co-Simulation and Validation in a 250 nm EPIC BiCMOS Technology. <i>Journal of Lightwave Technology</i>, <i>43</i>(1), 255–270. <a href=\"https://doi.org/10.1109/JLT.2024.3450949\">https://doi.org/10.1109/JLT.2024.3450949</a>","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.” <i>Journal of Lightwave Technology</i>, vol. 43, no. 1, 2025, pp. 255–70, doi:<a href=\"https://doi.org/10.1109/JLT.2024.3450949\">10.1109/JLT.2024.3450949</a>.","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={<a href=\"https://doi.org/10.1109/JLT.2024.3450949\">10.1109/JLT.2024.3450949</a>}, 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} }","short":"T. Schwabe, C. Kress, S. Kruse, M. Weizel, H. Rhee, J.C. Scheytt, Journal of Lightwave Technology 43 (2025) 255–270.","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,” <i>Journal of Lightwave Technology</i>, vol. 43, no. 1, pp. 255–270, 2025, doi: <a href=\"https://doi.org/10.1109/JLT.2024.3450949\">10.1109/JLT.2024.3450949</a>.","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.” <i>Journal of Lightwave Technology</i> 43, no. 1 (2025): 255–70. <a href=\"https://doi.org/10.1109/JLT.2024.3450949\">https://doi.org/10.1109/JLT.2024.3450949</a>.","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. <i>Journal of Lightwave Technology</i>. 2025;43(1):255-270. doi:<a href=\"https://doi.org/10.1109/JLT.2024.3450949\">10.1109/JLT.2024.3450949</a>"},"intvolume":"        43","page":"255-270","year":"2025","issue":"1"},{"main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2015/hey_icassp_2015.pdf","open_access":"1"}],"doi":"10.1109/ICASSP.2015.7178933","title":"Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions","date_created":"2019-07-12T05:28:45Z","author":[{"first_name":"Jahn","full_name":"Heymann, Jahn","id":"9168","last_name":"Heymann"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"},{"full_name":"Golik, P.","last_name":"Golik","first_name":"P."},{"first_name":"R.","full_name":"Schlueter, R.","last_name":"Schlueter"}],"date_updated":"2022-01-06T06:51:09Z","oa":"1","citation":{"apa":"Heymann, J., Haeb-Umbach, R., Golik, P., &#38; Schlueter, R. (2015). Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions. In <i>Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on</i> (pp. 5053–5057). <a href=\"https://doi.org/10.1109/ICASSP.2015.7178933\">https://doi.org/10.1109/ICASSP.2015.7178933</a>","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={<a href=\"https://doi.org/10.1109/ICASSP.2015.7178933\">10.1109/ICASSP.2015.7178933</a>}, 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} }","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.","mla":"Heymann, Jahn, et al. “Unsupervised Adaptation of a Denoising Autoencoder by Bayesian Feature Enhancement for Reverberant Asr under Mismatch Conditions.” <i>Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On</i>, 2015, pp. 5053–57, doi:<a href=\"https://doi.org/10.1109/ICASSP.2015.7178933\">10.1109/ICASSP.2015.7178933</a>.","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 <i>Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on</i>, 2015, pp. 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 <i>Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On</i>, 5053–57, 2015. <a href=\"https://doi.org/10.1109/ICASSP.2015.7178933\">https://doi.org/10.1109/ICASSP.2015.7178933</a>.","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: <i>Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On</i>. ; 2015:5053-5057. doi:<a href=\"https://doi.org/10.1109/ICASSP.2015.7178933\">10.1109/ICASSP.2015.7178933</a>"},"page":"5053-5057","year":"2015","language":[{"iso":"eng"}],"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"],"user_id":"44006","department":[{"_id":"54"}],"_id":"11813","status":"public","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."}],"type":"conference","publication":"Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on"},{"status":"public","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."}],"type":"conference","publication":"Prognostics and Health Management (PHM), 2014 IEEE Conference on","language":[{"iso":"eng"}],"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"],"user_id":"55222","department":[{"_id":"151"}],"_id":"9879","citation":{"bibtex":"@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics using particle filter with model parameter adaptation}, DOI={<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>}, 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} }","short":"J.K. Kimotho, T. Meyer, W. Sextro, 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.” <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>, 2014, pp. 1–6, doi:<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>.","apa":"Kimotho, J. K., Meyer, T., &#38; Sextro, W. (2014). PEM fuel cell prognostics using particle filter with model parameter adaptation. In <i>Prognostics and Health Management (PHM), 2014 IEEE Conference on</i> (pp. 1–6). <a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">https://doi.org/10.1109/ICPHM.2014.7036406</a>","ama":"Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter with model parameter adaptation. In: <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>. ; 2014:1-6. doi:<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>","ieee":"J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle filter with model parameter adaptation,” in <i>Prognostics and Health Management (PHM), 2014 IEEE Conference on</i>, 2014, pp. 1–6.","chicago":"Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” In <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>, 1–6, 2014. <a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">https://doi.org/10.1109/ICPHM.2014.7036406</a>."},"page":"1-6","year":"2014","doi":"10.1109/ICPHM.2014.7036406","title":"PEM fuel cell prognostics using particle filter with model parameter adaptation","date_created":"2019-05-20T13:11:02Z","author":[{"last_name":"Kimotho","full_name":"Kimotho, James Kuria ","first_name":"James Kuria "},{"first_name":"Tobias","full_name":"Meyer, Tobias","last_name":"Meyer"},{"first_name":"Walter","id":"21220","full_name":"Sextro, Walter","last_name":"Sextro"}],"date_updated":"2019-05-20T13:12:27Z"}]