@article{IskenVasyuraBathkeDahmetal.2022,
  author    = {Isken, Marius Paul and Vasyura-Bathke, Hannes and Dahm, Torsten and Heimann, Sebastian},
  title     = {De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter},
  series = {Geophysical journal international},
  volume    = {231},
  journal   = {Geophysical journal international},
  number    = {2},
  publisher = {Oxford University Press},
  address   = {Oxford},
  issn      = {0956-540X},
  doi       = {10.1093/gji/ggac229},
  pages     = {944 -- 949},
  year      = {2022},
  abstract  = {Data recorded by distributed acoustic sensing (DAS) along an optical fibre sample the spatial and temporal properties of seismic wavefields at high spatial density. Often leading to massive amount of data when collected for seismic monitoring along many kilometre long cables. The spatially coherent signals from weak seismic arrivals within the data are often obscured by incoherent noise. We present a flexible and computationally efficient filtering technique, which makes use of the dense spatial and temporal sampling of the data and that can handle the large amount of data. The presented adaptive frequency-wavenumber filter suppresses the incoherent seismic noise while amplifying the coherent wavefield. We analyse the response of the filter in time and spectral domain, and we demonstrate its performance on a noisy data set that was recorded in a vertical borehole observatory showing active and passive seismic phase arrivals. Lastly, we present a performant open-source software implementation enabling real-time filtering of large DAS data sets.},
  language  = {en}
}
@article{AndersMefenzaBobdaetal.2016,
  author    = {Anders, Jakob and Mefenza, Michael and Bobda, Christophe and Yonga, Franck and Aklah, Zeyad and Gunn, Kevin},
  title     = {A hardware/software prototyping system for driving assistance investigations},
  series = {Journal of real-time image processing},
  volume    = {11},
  journal   = {Journal of real-time image processing},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1861-8200},
  doi       = {10.1007/s11554-013-0351-4},
  pages     = {559 -- 569},
  year      = {2016},
  abstract  = {A holistic design and verification environment to investigate driving assistance systems is presented, with an emphasis on system-on-chip architectures for video applications. Starting with an executable specification of a driving assistance application, subsequent transformations are performed across different levels of abstraction until the final implementation is achieved. The hardware/software partitioning is facilitated through the integration of OpenCV and SystemC in the same design environment, as well as OpenCV and Linux in the run-time system. We built a rapid prototyping, FPGA-based camera system, which allows designs to be explored and evaluated in realistic conditions. Using lane departure and the corresponding performance speedup, we show that our platform reduces the design time, while improving the verification efforts.},
  language  = {en}
}
@article{EbertTrauth2015,
  author    = {Ebert, Thomas and Trauth, Martin H.},
  title     = {Semi-automated detection of annual laminae (varves) in lake sediments using a fuzzy logic algorithm},
  series = {Palaeogeography, palaeoclimatology, palaeoecology : an international journal for the geo-sciences},
  volume    = {435},
  journal   = {Palaeogeography, palaeoclimatology, palaeoecology : an international journal for the geo-sciences},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0031-0182},
  doi       = {10.1016/j.palaeo.2015.05.024},
  pages     = {272 -- 282},
  year      = {2015},
  abstract  = {Annual laminae (varves) in lake sediments are typically visually identified, measured and counted, although numerous attempts have been made to automate this process. The reason for the failure of most of these automated algorithms for varve counting is the complexity of the seasonal laminations, typically rich in lateral fades variations and internal heterogeneities. In the manual counting of varves, the investigator acquired and interpreted flexible numbers of complex decision criteria to understand whether a particular simple lamination is a varve or not. Fuzzy systems simulate the flexible decision making process in a computer by introducing a smooth transition between true varve and false varve. In our investigation, we use an adaptive neuro fuzzy inference system (ANFIS) to detect varves on the basis of a digital image of the sediment. The results of the application of the ANFIS to laminated sediments from the Meerfelder Maar (Eifel, Germany) and from a landslide-dammed lake in the Quebrada de Cafayate of Argentina are compared with manual varve counts and possible reasons for the differences are discussed. (C) 2015 Elsevier B.V. All rights reserved.},
  language  = {en}
}