Guided ultrasonic waves in long bones: modelling, experiment and in vivo application PHF Nicholson, P Moilanen, T Kärkkäinen, J Timonen, S Cheng Physiological measurement 23 (4), 755, 2002 | 200 | 2002 |

An enhanced memetic differential evolution in filter design for defect detection in paper production V Tirronen, F Neri, T Kärkkäinen, K Majava, T Rossi Evolutionary Computation 16 (4), 529-555, 2008 | 131 | 2008 |

Introduction to partitioning-based clustering methods with a robust example S Äyrämö, T Kärkkäinen Reports of the Department of Mathematical Information Technology. Series C …, 2006 | 103 | 2006 |

Assessment of the tibia using ultrasonic guided waves in pubertal girls P Moilanen, PHF Nicholson, T Kärkkäinen, Q Wang, J Timonen, S Cheng Osteoporosis international 14 (12), 1020-1027, 2003 | 91 | 2003 |

Augmented Lagrangian active set methods for obstacle problems T Kärkkäinen, K Kunisch, P Tarvainen Journal of optimization theory and applications 119 (3), 499-533, 2003 | 69 | 2003 |

Automated software license analysis T Tuunanen, J Koskinen, T Kärkkäinen Automated Software Engineering 16 (3-4), 455-490, 2009 | 55 | 2009 |

Robust formulations for training multilayer perceptrons T Kärkkäinen, E Heikkola Neural Computation 16 (4), 837-862, 2004 | 55 | 2004 |

Comparison of internal clustering validation indices for prototype-based clustering J Hämäläinen, S Jauhiainen, T Kärkkäinen Algorithms 10 (3), 105, 2017 | 52 | 2017 |

Fitness diversity based adaptation in multimeme algorithms: A comparative study F Neri, V Tirronen, T Karkkainen, T Rossi 2007 IEEE Congress on Evolutionary Computation, 2374-2381, 2007 | 50 | 2007 |

A memetic differential evolution in filter design for defect detection in paper production V Tirronen, F Neri, T Karkkainen, K Majava, T Rossi Workshops on Applications of Evolutionary Computation, 320-329, 2007 | 48 | 2007 |

Analysing student performance using sparse data of core bachelor courses M Saarela, T Kärkkäinen Journal of educational data mining 7 (1), 2015 | 44 | 2015 |

Denoising of Smooth Images Using *L*^{1}-FittingT Kärkkäinen, K Kunisch, K Majava Computing 74 (4), 353-376, 2005 | 43 | 2005 |

Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift M Pechenizkiy, J Bakker, I Žliobaitė, A Ivannikov, T Kärkkäinen ACM SIGKDD Explorations Newsletter 11 (2), 109-116, 2010 | 42 | 2010 |

Optimization of conducting structures by using the homogenization method J Haslinger, A Hillebrand, T Kärkkäinen, M Miettinen Structural and multidisciplinary optimization 24 (2), 125-140, 2002 | 38 | 2002 |

Independent component analysis on the mismatch negativity in an uninterrupted sound paradigm I Kalyakin, N González, T Kärkkäinen, H Lyytinen Journal of neuroscience methods 174 (2), 301-312, 2008 | 32 | 2008 |

Expert-based versus citation-based ranking of scholarly and scientific publication channels M Saarela, T Kärkkäinen, T Lahtonen, T Rossi Journal of Informetrics 10 (3), 693-718, 2016 | 31 | 2016 |

A New Augmented Lagrangian Approach for -mean Curvature Image Denoising M Myllykoski, R Glowinski, T Karkkainen, T Rossi SIAM Journal on Imaging Sciences 8 (1), 95-125, 2015 | 31 | 2015 |

Comparison of formulations and solution methods for image restoration problems T Kärkkäinen, K Majava, MM Mäkelä Inverse Problems 17 (6), 1977, 2001 | 29 | 2001 |

On the convergence of operator-splitting methods R Glowinski, T Kärkkäinen, K Majava Numerical Methods for Scientific Computing, Variational Problems and …, 2003 | 28 | 2003 |

The value of a real customer in a capstone project V Isomöttönen, T Kärkkäinen 2008 21st Conference on Software Engineering Education and Training, 85-92, 2008 | 26 | 2008 |