World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
39
Citations
5332
World Ranking
7777
National Ranking
2133

N.J. Bershad 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 N.J. Bershad 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: 199 publications — 48th percentile

48% of scientists in this discipline score the same or lower.

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

N.J. Bershad 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 N.J. Bershad 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: 39 D-Index — 24th percentile

24% of scientists in this discipline score the same or lower.

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

Overview

N.J. Bershad is affiliated with the University of California, Irvine, in the United States. Their research contributions primarily lie in the fields of Computer Science and Engineering, with significant work in related subfields such as Signal Processing, Computational Mechanics, Control and Systems Engineering, Artificial Intelligence, and Computer Vision and Pattern Recognition.

The scientist's research topics are diverse but focus on advanced technical aspects of signal processing and adaptive filtering. Key thematic areas in their work include:

  • Advanced Adaptive Filtering Techniques
  • Blind Source Separation Techniques
  • Speech and Audio Processing
  • Target Tracking and Data Fusion in Sensor Networks
  • Image and Signal Denoising Methods
  • Control Systems and Identification
  • Advanced Algorithms and Applications

Among the recent publications by N.J. Bershad are:

  • A switched variable step size NLMS adaptive filter, 2020, Digital Signal Processing
  • Stochastic analysis of the diffusion LMS algorithm for cyclostationary white Gaussian inputs, 2021, Signal Processing

Their frequent coauthors include J.C.M. Bermudez and Eweda Eweda. Bershad's collaborative efforts with these colleagues have resulted in multiple joint publications.

They have published extensively in a range of scholarly venues, with multiple papers appearing in:

  • Signal Processing
  • SSRN Electronic Journal
  • Digital Signal Processing
  • IEEE Transactions on Signal Processing
  • International Journal of Adaptive Control and Signal Processing

N.J. Bershad's work involves the development and stochastic analysis of adaptive algorithms, particularly the least mean square (LMS) family and related variants applied to cyclostationary and Gaussian inputs. Their research explores algorithm performance under practical network conditions, including communication delays and colored noise environments.

Best Publications

  • Random differential equations in science and engineering

    Unknown

  • Analysis of the normalized LMS algorithm with Gaussian inputs

    Unknown

  • Time delay estimation using the LMS adaptive filter--Dynamic behavior

    Unknown

  • An Affine Combination of Two LMS Adaptive Filters—Transient Mean-Square Analysis

    N.J. Bershad;J.C.M. Bermudez;J.-Y. Tourneret

  • Adaptive recovery of a chirped sinusoid in noise. II. Performance of the LMS algorithm

    N.J. Bershad;O.M. Macchi

  • Mean weight behavior of the filtered-X LMS algorithm

    O.J. Tobias;J.C.M. Bermudez;N.J. Bershad

  • Comments on and additions to "An adaptive recursive LMS filter"

    C.R. Johnson;M.G. Larimore;P.L. Feintuch;N.J. Bershad

  • A statistical analysis of the affine projection algorithm for unity step size and autoregressive inputs

    S.J.M. de Almeida;J.C.M. Bermudez;N.J. Bershad;M.H. Costa

  • A frequency domain model for 'filtered' LMS algorithms-stability analysis, design, and elimination of the training mode

    Unknown

  • Neural networks for modeling nonlinear memoryless communication channels

    Unknown

  • Stochastic analysis of the filtered-X LMS algorithm in systems with nonlinear secondary paths

    M.H. Costa;J.C.M. Bermudez;N.J. Bershad

  • Stochastic Analysis of a Stable Normalized Least Mean Fourth Algorithm for Adaptive Noise Canceling With a White Gaussian Reference

    E. Eweda;N. J. Bershad

  • Analysis of stochastic gradient identification of Wiener-Hammerstein systems for nonlinearities with Hermite polynomial expansions

    N.J. Bershad;P. Celka;S. McLaughlin

  • Neural network modeling and identification of nonlinear channels with memory: algorithms, applications, and analytic models

    Unknown

  • On error-saturation nonlinearities in LMS adaptation

    Unknown

  • Stochastic analysis of gradient adaptive identification of nonlinear systems with memory for Gaussian data and noisy input and output measurements

    N.J. Bershad;P. Celka;J.-M. Vesin

  • Stochastic gradient identification of polynomial Wiener systems: analysis and application

    P. Celka;N.J. Bershad;J.-M. Vesin

  • The complex LMS adaptive algorithm--Transient weight mean and covariance with applications to the ALE

    Unknown

  • Stochastic Analysis of the LMS and NLMS Algorithms for Cyclostationary White Gaussian Inputs

    Unknown

  • Tracking characteristics of the LMS adaptive line enhancer-response to a linear chirp signal in noise

    Unknown

  • Statistical analysis of the single-layer backpropagation algorithm. II. MSE and classification performance

    N.J. Bershad;J.J. Shynk;P.L. Feintuch

  • Statistical analysis of the single-layer backpropagation algorithm. I. mean weight behavior

    N.J. Bershad;J.J. Shynk;P.L. Feintuch

  • A nonlinear analytical model for the quantized LMS algorithm-the arbitrary step size case

    J.C.M. Bermudez;N.J. Bershad

  • Analytic behavior of the LMS adaptive line enhancer for sinusoids corrupted by multiplicative and additive noise

    M. Ghogho;M. Ibnkahla;N.J. Bershad

  • Stochastic analysis of adaptive gradient identification of Wiener-Hammerstein systems for Gaussian inputs

    N.J. Bershad;S. Bouchired;F. Castanie

  • Fast coupled adaptation for sparse impulse responses using a partial haar transform

    N.J. Bershad;A. Bist

  • Stochastic analysis of the LMS algorithm with a saturation nonlinearity following the adaptive filter output

    M.H. Costa;J.C.M. Bermudez;N.J. Bershad

  • Stochastic Analysis of the LMS Algorithm for System Identification With Subspace Inputs

    N.J. Bershad;J.C.M. Bermudez;J.-Y. Tourneret

  • Performance comparison of RLS and LMS algorithms for tracking a first order Markov communications channel

    N.J. Bershad;S. McLaughlin;C.F.N. Cowan

  • Mean weight behavior of the Filtered-X LMS algorithm

    O.J. Tobias;J.C.M. Bermudez;N.J. Bershad;R. Seara

Frequent Co-Authors

Jean-Yves Tourneret
Jean-Yves Tourneret National Polytechnic Institute of Toulouse
Stephen McLaughlin
Stephen McLaughlin Heriot-Watt University
Suhas Diggavi
Suhas Diggavi University of California, Los Angeles

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