@InProceedings{6952760,
  author = {B. Jokanovic and M. G. Amin and Y. D. Zhang and F. Ahmad},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Time-frequency kernel design for sparse joint-variable signal representations},
  year = {2014},
  pages = {2100-2104},
  abstract = {Highly localized quadratic time-frequency distributions cast nonstationary signals as sparse in the joint-variable representations. The linear model relating the ambiguity domain and time-frequency domain permits the application of sparse signal reconstruction techniques to yield high-resolution time-frequency representations. In this paper, we design signal-dependent kernels that enable the resulting time-frequency distribution to meet the two objectives of reduced cross-term interference and increased sparsity. It is shown that, for random undersampling schemes, the new adaptive kernel is superior to traditional reduced interference distribution kernels.},
  keywords = {interference (signal);signal representation;time-frequency analysis;time-frequency kernel design;sparse joint-variable signal representations;highly localized quadratic time-frequency distributions;nonstationary signals;time-frequency domain;ambiguity domain;signal-dependent kernels;cross-term interference;Kernel;Time-frequency analysis;Interference;Compressed sensing;Signal representation;Optimization;Linear programming;Kernel design;reduced interference distribution;sparse representation;time-frequency analysis},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569923333.pdf},
}

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Ahmad},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Time-frequency kernel design for sparse joint-variable signal representations},\n year = {2014},\n pages = {2100-2104},\n abstract = {Highly localized quadratic time-frequency distributions cast nonstationary signals as sparse in the joint-variable representations. The linear model relating the ambiguity domain and time-frequency domain permits the application of sparse signal reconstruction techniques to yield high-resolution time-frequency representations. In this paper, we design signal-dependent kernels that enable the resulting time-frequency distribution to meet the two objectives of reduced cross-term interference and increased sparsity. 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