World's Best Scientists 2026 revealed!
Seppo J. Ovaska

Seppo J. Ovaska

D-Index & Metrics

Engineering and Technology

D-Index
35
Citations
4961
World Ranking
8989
National Ranking
55

Seppo J. Ovaska publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Seppo J. Ovaska sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 330 publications — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Seppo J. Ovaska D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Seppo J. Ovaska sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 35 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Electrical engineering
  • Machine learning

His main research concerns Artificial intelligence, Control theory, Filter, Machine learning and Digital filter. In his study, Sampling and Signal is strongly linked to Algorithm, which falls under the umbrella field of Artificial intelligence. He studies Adaptive filter which is a part of Control theory.

His work carried out in the field of Filter brings together such families of science as Rayleigh fading, Quadrature and Power control. His Machine learning research includes themes of Iris flower data set and Fuzzy classification systems. His Infinite impulse response study in the realm of Digital filter interacts with subjects such as Median filter.

His most cited work include:

  • Noise reduction in zero crossing detection by predictive digital filtering (147 citations)
  • Industrial applications of soft computing: a review (131 citations)
  • Angular acceleration measurement: a review (102 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Control theory, Artificial intelligence, Artificial neural network, Soft computing and Algorithm. His Control theory study incorporates themes from Digital filter, Polynomial and Filter. His research integrates issues of Low-pass filter, Linear filter and Signal processing in his study of Digital filter.

His Artificial intelligence study frequently links to other fields, such as Machine learning. His Artificial neural network research is multidisciplinary, relying on both Fault, Fault detection and isolation, Nonlinear system and Power control. His studies in Soft computing integrate themes in fields like Computational intelligence, Genetic algorithm, Hybrid system, Intelligent control and Intelligent decision support system.

He most often published in these fields:

  • Control theory (32.13%)
  • Artificial intelligence (22.38%)
  • Artificial neural network (17.33%)

What were the highlights of his more recent work (between 2007-2019)?

  • Artificial intelligence (22.38%)
  • Mathematical optimization (10.11%)
  • Harmony search (4.69%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Artificial intelligence, Mathematical optimization, Harmony search, Artificial immune system and Electronic engineering. The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition. His work investigates the relationship between Artificial immune system and topics such as Fault detection and isolation that intersect with problems in Anomaly detection.

His studies deal with areas such as Control theory, Pulse generator, Active filter and Electrical engineering as well as Electronic engineering. In his work, Fundamental frequency is strongly intertwined with Signal generator, which is a subfield of Control theory. Artificial neural network is closely attributed to Algorithm in his work.

Between 2007 and 2019, his most popular works were:

  • A general framework for statistical performance comparison of evolutionary computation algorithms (92 citations)
  • Real-Time Systems Design and Analysis : Tools for the Practitioner (63 citations)
  • UNI-MODAL AND MULTI-MODAL OPTIMIZATION USING MODIFIED HARMONY SEARCH METHODS (53 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Electrical engineering
  • Machine learning

His primary areas of investigation include Harmony search, Artificial intelligence, Mathematical optimization, Artificial immune system and Metaheuristic. His Harmony search research incorporates themes from Optimization algorithm, Optimization problem and Nonlinear system. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.

His research is interdisciplinary, bridging the disciplines of Fuzzy logic and Machine learning. As part of one scientific family, Seppo J. Ovaska deals mainly with the area of Artificial immune system, narrowing it down to issues related to the Detector, and often Algorithm, Fault and Artificial neural network. The Metaheuristic study which covers Evolutionary computation that intersects with Genetic algorithm and Variety.

Best Publications

  • Noise reduction in zero crossing detection by predictive digital filtering

    O. Vainio;S.J. Ovaska

  • Industrial applications of soft computing: a review

    Y. Dote;S.J. Ovaska

  • Angular acceleration measurement: a review

    S.J. Ovaska;S. Valiviita

  • A general framework for statistical performance comparison of evolutionary computation algorithms

    David Shilane;Jarno Martikainen;Sandrine Dudoit;Seppo J. Ovaska

  • Digital filtering for robust 50/60 Hz zero-crossing detectors

    O. Vainio;S.J. Ovaska

  • A modified Elman neural network model with application to dynamical systems identification

    X.Z. Gao;X.M. Gao;S.J. Ovaska

  • Polynomial predictive filtering in control instrumentation: a review

    S. Valiviita;S.J. Ovaska;O. Vainio

  • Soft Computing in Industrial Applications

    Yukinori Suzuki;S. J. Ovaska;Y. Dote;R. Roy

  • Delta operator realizations of direct-form IIR filters

    Juha Kauraniemi;T.I. Laakso;I. Hartimo;S.J. Ovaska

  • Soft computing methods in motor fault diagnosis

    Xiao Zhi Gao;Seppo J. Ovaska

  • Artificial immune optimization methods and applications - a survey

    X. Wang;X.Z. Gao;S.J. Ovaska

  • Real-Time Systems Design and Analysis : Tools for the Practitioner

    Phillip A. Laplante;Seppo J. Ovaska

  • Reference signal generator for active power filters using improved adaptive predictive filter

    Byung-Moon Han;Byong-Yeul Bae;S.J. Ovaska

  • Delayless method to generate current reference for active filters

    S. Valiviita;S.J. Ovaska

  • Power prediction in mobile communication systems using an optimal neural-network structure

    X.M. Gao;X.Z. Gao;J.M.A. Tanskanen;S.J. Ovaska

  • Improving the velocity sensing resolution of pulse encoders by FIR prediction

    S.J. Ovaska

  • Adaptive filtering using multiplicative general parameters for zero-crossing detection

    O. Vainio;S.J. Ovaska;M. Polla

  • Fusion of soft computing and hard computing in industrial applications: an overview

    S.J. Ovaska;H.F. VanLandingham;A. Kamiya

  • UNI-MODAL AND MULTI-MODAL OPTIMIZATION USING MODIFIED HARMONY SEARCH METHODS

    Xiaozhi Gao;Xiaolei Wang;Seppo Ovaska

  • Multistage adaptive filters for in-phase processing of line-frequency signals

    O. Vainio;S.J. Ovaska

  • Soft Computing and Industry

    Rajkumar Roy;Mario Köppen;Seppo Ovaska;Takeshi Furuhashi

Frequent Co-Authors

Xiao-Zhi Gao
Xiao-Zhi Gao University of Eastern Finland
Bernhard Sick
Bernhard Sick University of Kassel
Rajkumar Roy
Rajkumar Roy City, University of London
Antero Arkkio
Antero Arkkio Aalto University
Mo-Yuen Chow
Mo-Yuen Chow North Carolina State University
Athanasios V. Vasilakos
Athanasios V. Vasilakos University of Agder
YangQuan Chen
YangQuan Chen University of California, Merced
Vesa Välimäki
Vesa Välimäki Aalto University
Teuvo Suntio
Teuvo Suntio Tampere University
Hannu Tenhunen
Hannu Tenhunen Royal Institute of Technology

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