TY  - JOUR
A1  - Isken, Marius Paul
A1  - Vasyura-Bathke, Hannes
A1  - Dahm, Torsten
A1  - Heimann, Sebastian
T1  - De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter
JF  - Geophysical journal international
N2  - 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.
KW  - Fourier analysis
KW  - Image processing
KW  - Time-series analysis
KW  - Seismic noise
KW  - Distributed acoustic sensing
Y1  - 2022
U6  - https://doi.org/10.1093/gji/ggac229
SN  - 0956-540X
SN  - 1365-246X
VL  - 231
IS  - 2
SP  - 944
EP  - 949
PB  - Oxford University Press
CY  - Oxford
ER  - 
TY  - JOUR
A1  - Anders, Jakob
A1  - Mefenza, Michael
A1  - Bobda, Christophe
A1  - Yonga, Franck
A1  - Aklah, Zeyad
A1  - Gunn, Kevin
T1  - A hardware/software prototyping system for driving assistance investigations
JF  - Journal of real-time image processing
N2  - 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.
KW  - System on chip
KW  - Prototyping
KW  - Hardware/software system
KW  - Image processing
KW  - Design flow
KW  - Driver assistance
KW  - FPGA
KW  - Hardware acceleration
Y1  - 2016
U6  - https://doi.org/10.1007/s11554-013-0351-4
SN  - 1861-8200
SN  - 1861-8219
VL  - 11
SP  - 559
EP  - 569
PB  - Springer
CY  - Heidelberg
ER  - 
TY  - JOUR
A1  - Ebert, Thomas
A1  - Trauth, Martin H.
T1  - Semi-automated detection of annual laminae (varves) in lake sediments using a fuzzy logic algorithm
JF  - Palaeogeography, palaeoclimatology, palaeoecology : an international journal for the geo-sciences
N2  - 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.
KW  - Varve
KW  - Lake sediment
KW  - Image processing
KW  - Fuzzy logic
KW  - MATLAB
KW  - Statistics
Y1  - 2015
U6  - https://doi.org/10.1016/j.palaeo.2015.05.024
SN  - 0031-0182
SN  - 1872-616X
VL  - 435
SP  - 272
EP  - 282
PB  - Elsevier
CY  - Amsterdam
ER  -