What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Fourier transform
His primary scientific interests are in Fast Fourier transform, Rader's FFT algorithm, Cyclotomic fast Fourier transform, Split-radix FFT algorithm and Prime-factor FFT algorithm.
His Rader's FFT algorithm research integrates issues from Discrete Hartley transform and Arithmetic.
His Discrete Hartley transform study which covers Convolution that intersects with Hartley transform and Overlap–add method.
Bruun's FFT algorithm, Bluestein's FFT algorithm and Applied mathematics is closely connected to Discrete Fourier transform in his research, which is encompassed under the umbrella topic of Cyclotomic fast Fourier transform.
His Split-radix FFT algorithm study integrates concerns from other disciplines, such as Algorithm and Twiddle factor.
C.S. Burrus interconnects Discrete wavelet transform, Stationary wavelet transform, Wavelet packet decomposition, Cascade algorithm and Pure mathematics in the investigation of issues within Algorithm.
His most cited work include:
- Introduction to Wavelets and Wavelet Transforms: A Primer (1429 citations)
- Digital Filter Design (816 citations)
- Noise reduction using an undecimated discrete wavelet transform (438 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Algorithm, Wavelet, Fast Fourier transform, Wavelet transform and Finite impulse response.
His Algorithm study combines topics from a wide range of disciplines, such as Convolution, Mathematical optimization and Discrete Fourier transform.
His study in Wavelet concentrates on Discrete wavelet transform, Wavelet packet decomposition, Cascade algorithm and Multiresolution analysis.
C.S. Burrus has included themes like Cyclotomic fast Fourier transform, Split-radix FFT algorithm, Prime-factor FFT algorithm, Arithmetic and Rader's FFT algorithm in his Fast Fourier transform study.
He works mostly in the field of Wavelet transform, limiting it down to topics relating to Discrete mathematics and, in certain cases, Polynomial, as a part of the same area of interest.
His Finite impulse response research is multidisciplinary, incorporating perspectives in Frequency response, Algorithm design and Passband.
He most often published in these fields:
- Algorithm (50.94%)
- Wavelet (31.13%)
- Fast Fourier transform (28.30%)
What were the highlights of his more recent work (between 1998-2009)?
- Algorithm (50.94%)
- Wavelet (31.13%)
- Wavelet transform (26.42%)
In recent papers he was focusing on the following fields of study:
C.S. Burrus focuses on Algorithm, Wavelet, Wavelet transform, Discrete wavelet transform and Computer vision.
His work in the fields of Algorithm, such as Finite impulse response, overlaps with other areas such as Filter bank.
His Wavelet study frequently draws parallels with other fields, such as Cepstrum.
His study in the field of Stationary wavelet transform, Wavelet packet decomposition, Harmonic wavelet transform and Second-generation wavelet transform also crosses realms of Electronic mail.
C.S. Burrus works in the field of Discrete wavelet transform, focusing on Lifting scheme in particular.
His work deals with themes such as Low-pass filter, Prototype filter, High-pass filter and Speech recognition, which intersect with Lifting scheme.
Between 1998 and 2009, his most popular works were:
- A new framework for complex wavelet transforms (113 citations)
- Factoring very-high-degree polynomials (66 citations)
- Speckle Reduction via Wavelet Shrinkage with Application to SAR based ATD/R (32 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Artificial intelligence
- Fourier transform
C.S. Burrus mainly investigates Multimedia, Information technology, Computer vision, Artificial intelligence and Wavelet.
His Multimedia study frequently links to related topics such as Embodied cognition.
His Information technology research includes a combination of various areas of study, such as Quality, Digital signal processing, Individual learning and XML.
Many of his studies on Computer vision involve topics that are commonly interrelated, such as Detection performance.
His is doing research in Speckle noise, Wavelet packet decomposition, Discrete wavelet transform, Complex wavelet transform and Harmonic wavelet transform, both of which are found in Artificial intelligence.
His Wavelet study frequently draws connections to other fields, such as Algorithm.
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