@inproceedings{2086,
  abstract     = {{Many of the existing fusion approaches based on Dempster-Shafer Theory (DST) tend to be unreliable in various scenarios. Therefore, this topic is still in discussion. In this work a Two-Layer Conflict Solving (TLCS) data fusion scheme is proposed which is based on Dempster-Shafer Theory and on Fuzzy-Pattern-Classification (FPC) concepts. The aim is to provide an approach to data fusion which provides a stable conflict scenario handling. Furthermore, the scheme can easily be extended to fuzzy classification and is applicable to sensor fusion applications. Therefore, the suggested approach will contribute as a novel fuzzy fusion method.}},
  author       = {{Lohweg, Volker and Mönks, Uwe}},
  booktitle    = {{The 2nd International Workshop on Cognitive Information Processing}},
  isbn         = {{978-1-4244-6457-9}},
  issn         = {{2327-1671 }},
  keywords     = {{Noise measurement, Fuzzy sets, Noise, Sensor fusion, Logic gates, Feature extraction, Fuses}},
  location     = {{Elba}},
  publisher    = {{14-16 June, 2010, Elba Island (Tuscany), Italy}},
  title        = {{{Sensor Fusion by Two-Layer Conflict Solving}}},
  doi          = {{10.1109/CIP.2010.5604094}},
  year         = {{2010}},
}

@article{2056,
  abstract     = {{Nonlinear spatial transforms and fuzzy pattern classification with unimodal potential functions are established in signal processing. They have proved to be excellent tools in feature extraction and classification. In this paper, we will present a hardware-accelerated image processing and classification system which is implemented on one field-programmable gate array (FPGA). Nonlinear discrete circular transforms generate a feature vector. The features are analyzed by a fuzzy classifier. This principle can be used for feature extraction, pattern recognition, and classification tasks. Implementation in radix-2 structures is possible, allowing fast calculations with a computational complexity of up to. Furthermore, the pattern separability properties of these transforms are better than those achieved with the well-known method based on the power spectrum of the Fourier Transform, or on several other transforms. Using different signal flow structures, the transforms can be adapted to different image and signal processing applications.}},
  author       = {{Lohweg, Volker and Diederichs, Carsten and Müller, Dietmar}},
  issn         = {{1110-8657 }},
  journal      = {{EURASIP journal on applied signal processing : a publication of the European Association for Speech, Signal, and Image Processing }},
  keywords     = {{image processing, nonlinear circular transforms, feature extraction, fuzzy pattern recognition}},
  number       = {{1}},
  pages        = {{1912--1920}},
  publisher    = {{Hindawi Publ.}},
  title        = {{{Algorithms for Hardware-Based Pattern Recognition}}},
  doi          = {{https://doi.org/10.1155/S1110865704404247}},
  volume       = {{12}},
  year         = {{2004}},
}