What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Mathematical analysis
Algorithm, Hilbert–Huang transform, Wavelet, Time–frequency analysis and Signal processing are his primary areas of study.
His study looks at the intersection of Algorithm and topics like Speech recognition with Reassignment method, Bilinear interpolation and Distribution.
The various areas that Patrick Flandrin examines in his Hilbert–Huang transform study include Statistical physics, Mathematical optimization and Gaussian noise.
His Wavelet study incorporates themes from Discretization, Fractal, Hurst exponent and Scaling.
His Time–frequency analysis research is multidisciplinary, incorporating perspectives in Quadratic equation, Statistics and Frequency modulation.
His research integrates issues of Simulation, Transport engineering, Public transport and Applied mathematics in his study of Signal processing.
His most cited work include:
- Empirical mode decomposition as a filter bank (1778 citations)
- On empirical mode decomposition and its algorithms (1097 citations)
- Improving the readability of time-frequency and time-scale representations by the reassignment method (907 citations)
What are the main themes of his work throughout his whole career to date?
Patrick Flandrin mainly investigates Time–frequency analysis, Algorithm, Signal processing, Hilbert–Huang transform and Wavelet.
His Time–frequency analysis research integrates issues from Affine transformation, Plane and Artificial intelligence, Spectrogram.
His Gaussian noise study in the realm of Algorithm interacts with subjects such as Structure.
His work carried out in the field of Signal processing brings together such families of science as Distribution, Econometrics and Pattern recognition.
His Hilbert–Huang transform study combines topics from a wide range of disciplines, such as Mathematical optimization and Data mining.
He interconnects Fractal, Mathematical analysis, Statistical physics and Estimator in the investigation of issues within Wavelet.
He most often published in these fields:
- Time–frequency analysis (32.39%)
- Algorithm (31.25%)
- Signal processing (17.61%)
What were the highlights of his more recent work (between 2012-2021)?
- Time–frequency analysis (32.39%)
- Algorithm (31.25%)
- Theoretical computer science (7.39%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Time–frequency analysis, Algorithm, Theoretical computer science, Hilbert–Huang transform and Discrete mathematics.
His studies deal with areas such as Speech recognition, Spectrogram, Signal processing, Electronic engineering and Kernel as well as Time–frequency analysis.
His biological study deals with issues like Gaussian noise, which deal with fields such as Filter bank, Multivariate statistics and Distribution.
His Signal processing study integrates concerns from other disciplines, such as Multidimensional scaling and Duality.
His Algorithm research incorporates themes from Mathematical optimization, Signal and Fourier transform.
His study on Hilbert–Huang transform is covered under Mode.
Between 2012 and 2021, his most popular works were:
- Time-Frequency Reassignment and Synchrosqueezing: An Overview (265 citations)
- Trend filtering via empirical mode decompositions (69 citations)
- Strip, bind, and search: a method for identifying abnormal energy consumption in buildings (58 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Mathematical analysis
The scientist’s investigation covers issues in Time–frequency analysis, Algorithm, Fourier transform, Spectrogram and Hilbert–Huang transform.
His Time–frequency analysis research is multidisciplinary, relying on both Epistemology, Speech recognition and Signal processing.
His Algorithm research is multidisciplinary, incorporating elements of Noise and Outlier.
His Fourier transform research incorporates elements of Time–frequency representation, Delaunay triangulation, Combinatorics and Gaussian noise.
His Spectrogram research incorporates themes from Gabor–Wigner transform, Gabor transform and Mathematical analysis, Hermite polynomials.
The study incorporates disciplines such as Applied mathematics and Seasonality in addition to Hilbert–Huang transform.
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