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Advanced Adaptive Filtering Techniques

Advanced Adaptive Filtering Techniques is a research topic within Computational Mechanics. Science Explorer counts 30k research works in it since 1950. 9.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the topic of adaptive filtering in non-Gaussian signal processing, with an emphasis on diffusion strategies, kernel algorithms, sparse system identification, active noise control, distributed estimation, variable step-size algorithms, robust adaptive filtering, and the application of correntropy criterion. The papers cover a wide range of applications and performance analyses for adaptive filtering techniques in complex and non-Gaussian signal environments.

  • Adaptive Filtering
  • Non-Gaussian Signal Processing
  • Diffusion Strategies
  • Kernel Algorithms
  • Sparse System Identification
  • Active Noise Control
  • Distributed Estimation
  • Variable Step-Size Algorithms
  • Robust Adaptive Filtering
  • Correntropy Criterion
Research works
30k
fractional, since 1950
In the world top 10%
2.9k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
0%
the tick is no change
Largest producer
China
53% of the 10 largest, 2022-now
Advanced Adaptive Filtering Techniques

Which countries lead Advanced Adaptive Filtering Techniques research?

By volume, China and India publish the most (1.4k and 362 works in 2022-now).

By volume, 2022-now

  1. 1 China 1.4k works
  2. 2 India 362 works
  3. 3 United States 246 works
  4. 4 Japan 161 works
  5. 5 United Kingdom 105 works
  6. 6 Germany 105 works
  7. 7 France 64 works
  8. 8 Canada 64 works
  9. 9 South Korea 63 works
  10. 10 Brazil 47 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 53.3%India: 13.9%United States: 9.5%Japan: 6.2%6 others listed: 17.2%53%largest
China1,386 · 53.3%India362 · 13.9%United States246 · 9.5%Japan161 · 6.2%6 others listed447 · 17.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Advanced Adaptive Filtering Techniques research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Advanced Adaptive Filtering Techniques research, followed by Southwest Jiaotong University and University of Electronic Science and Technology of China.

Who are the leading researchers in Advanced Adaptive Filtering Techniques?

The most-cited researchers publishing on Advanced Adaptive Filtering Techniques include Wei Huang, Xiaogang Wang and Trevor J. Darrell.

  1. 1 Wei Huang China 267k citations
  2. 2 Xiaogang Wang China 260k citations
  3. 3 Trevor J. Darrell United States 206k citations
  4. 4 H. Vincent Poor United States 165k citations
  5. 5 Anil Kumar Jain United States 160k citations
  6. 6 Bernhard Schölkopf Germany 148k citations
  7. 7 Stephen Boyd United States 142k citations
  8. 8 Francisco Herrera Spain 141k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Advanced Adaptive Filtering Techniques?

The most cited paper in Advanced Adaptive Filtering Techniques is "Cognitive radio: brain-empowered wireless communications" (S. Haykin, 2005), with 12,031 citations. Next come "Savitzky-Golay Smoothing Filters" (11,772) and "Suppression of acoustic noise in speech using spectral subtraction" (4,674).

Most cited papers
#PaperReleasedCitations
1 Cognitive radio: brain-empowered wireless communications S. Haykin · IEEE Journal on Selected Areas in Communications Feb 2005 12,031
2 Savitzky-Golay Smoothing Filters William H. Press, Saul A. Teukolsky · Computers in Physics Open access Nov 1990 11,772
3 Suppression of acoustic noise in speech using spectral subtraction S. Boll · IEEE Transactions on Acoustics Speech and Signal Processing Apr 1979 4,674
4 Adaptive filtering, prediction and control Pétros Ioannou · Automatica Sep 1985 4,490
5 The generalized correlation method for estimation of time delay C. Knapp, G. Carter · IEEE Transactions on Acoustics Speech and Signal Processing Aug 1976 4,441
All 99 most cited papers, with filters and sorting

Where is Advanced Adaptive Filtering Techniques research done?

The largest centres of Advanced Adaptive Filtering Techniques research in 2022-now are Beijing (China), Chengdu (China), Xi'an (China) and Nanjing (China).

Largest cities, 2022-now

  1. 1 BeijingChina 219 works
  2. 2 ChengduChina 130 works
  3. 3 Xi'anChina 115 works
  4. 4 NanjingChina 92 works
  5. 5 ShanghaiChina 72 works
  6. 6 TokyoJapan 70 works
  7. 7 HarbinChina 63 works
  8. 8 ChongqingChina 61 works
  9. 9 WuhanChina 61 works
  10. 10 HangzhouChina 38 works
See Advanced Adaptive Filtering Techniques on the map

Where is the best place to study Advanced Adaptive Filtering Techniques?

Among universities, judged by research, Southwest Jiaotong University, Nanyang Technological University and Qingdao University of Science and Technology score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 21.19%fractional works in this field (log) →share of work in the world top 10% →Qingdao University o…Nanyang Technologica…Southwest Jiaotong U…Northwestern Polytec…
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Southwest Jiaotong UniversityChina 71.318.9%23.1×51 +27.5%
2Nanyang Technological UniversitySingapore 66.935.1%8.3×19 +15.3%
3Qingdao University of Science and TechnologyChina 65.340.7%9.9×10 +68.3%
4Northwestern Polytechnical UniversityChina 60.417.2%13.5×45 +14.1%
5University of Electronic Science and Technology of ChinaChina 60.017.7%15.0×49 +4.8%
6Southwest UniversityChina 57.916.8%17.5×28 +20.7%
7University of SouthamptonUnited Kingdom 53.812.1%11.5×17 +68.0%
8Jiangnan UniversityChina 53.023.6%7.3×12 +30.4%
9Aalborg UniversityDenmark 47.010.7%9.2×15 +19.7%
10Institut National de la Recherche ScientifiqueCanada 46.319.1%54.7×12 -2.1%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Advanced Adaptive Filtering Techniques, country by country

Is Advanced Adaptive Filtering Techniques research growing?

Output in 2022-2025 was 0% lower than in 2018-2021, peaking in 2002.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Advanced Adaptive Filtering Techniques as a percentage of every work published that year, 0.016% in 2024.

still counting0.050%0.10%0.15%Advanced Adaptive Filtering Techniques0.013% in 202519801990200020102020

Fractional works of Advanced Adaptive Filtering Techniques over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Advanced Adaptive Filtering Techniques9.8% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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