What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
Boualem Boashash mainly focuses on Instantaneous phase, Time–frequency analysis, Algorithm, Signal processing and Signal.
The study incorporates disciplines such as Estimation theory, Statistics, Kernel and Polynomial in addition to Instantaneous phase.
His Time–frequency analysis research integrates issues from Electroencephalography, Speech recognition, Source separation, Artificial intelligence and Separable space.
His work deals with themes such as Filter, Blind signal separation, Additive white Gaussian noise, Frequency modulation and Signal-to-noise ratio, which intersect with Algorithm.
Boualem Boashash combines subjects such as Distribution, Electronic engineering, Domain and Pattern recognition with his study of Signal processing.
In Signal, Boualem Boashash works on issues like Acoustics, which are connected to Observer and Time frequency signal analysis.
His most cited work include:
- Estimating and interpreting the instantaneous frequency of a signal. I. Fundamentals (1557 citations)
- A human identification technique using images of the iris and wavelet transform (1028 citations)
- Time-Frequency Signal Analysis and Processing: A Comprehensive Reference (783 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Time–frequency analysis, Speech recognition, Artificial intelligence, Algorithm and Instantaneous phase.
Boualem Boashash has researched Time–frequency analysis in several fields, including Quadratic equation, Frequency domain, Spectrogram and Signal processing.
His biological study deals with issues like Electroencephalography, which deal with fields such as Matching pursuit and Artificial neural network.
His research in Artificial intelligence intersects with topics in Computer vision and Pattern recognition.
His work on Time complexity as part of general Algorithm research is frequently linked to Wigner distribution function, bridging the gap between disciplines.
His Instantaneous phase research focuses on Statistics and how it connects with Applied mathematics.
He most often published in these fields:
- Time–frequency analysis (47.89%)
- Speech recognition (35.74%)
- Artificial intelligence (32.75%)
What were the highlights of his more recent work (between 2011-2021)?
- Time–frequency analysis (47.89%)
- Artificial intelligence (32.75%)
- Speech recognition (35.74%)
In recent papers he was focusing on the following fields of study:
Boualem Boashash focuses on Time–frequency analysis, Artificial intelligence, Speech recognition, Pattern recognition and Electroencephalography.
His biological study spans a wide range of topics, including Algorithm, Instantaneous phase, Blind signal separation and Signal processing.
Boualem Boashash interconnects Quadratic equation and Computer vision in the investigation of issues within Artificial intelligence.
The various areas that Boualem Boashash examines in his Speech recognition study include Feature, Digital signal processing, Coherence and Detector.
Boualem Boashash combines subjects such as Representation and Seizure detection with his study of Pattern recognition.
His work in Electroencephalography tackles topics such as Matched filter which are related to areas like Matching pursuit.
Between 2011 and 2021, his most popular works were:
- Time-Frequency Signal Analysis and Processing: A Comprehensive Reference (783 citations)
- 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data (124 citations)
- 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data (124 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
The scientist’s investigation covers issues in Time–frequency analysis, Artificial intelligence, Pattern recognition, Algorithm and Speech recognition.
His studies in Time–frequency analysis integrate themes in fields like Instantaneous phase, Coherence, Robustness and Electroencephalography.
The Artificial intelligence study combines topics in areas such as Quadratic equation, Computer vision and Signal processing.
His research integrates issues of Emerging technologies, Systems engineering and Key in his study of Signal processing.
His study in Algorithm is interdisciplinary in nature, drawing from both Mathematical optimization and Spectrogram.
His Speech recognition research is multidisciplinary, relying on both Digital signal processing and Visual inspection.
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