What is he best known for?
The fields of study he is best known for:
- Statistics
- Algorithm
- Telecommunications
His scientific interests lie mostly in Algorithm, Subspace topology, Estimation theory, Blind signal separation and Signal processing.
His biological study spans a wide range of topics, including Sequence, Distribution and Time–frequency analysis.
Karim Abed-Meraim has included themes like Mathematical optimization, Linear subspace, Noise and Pattern recognition in his Subspace topology study.
Karim Abed-Meraim has researched Blind signal separation in several fields, including Statistics and Source separation.
His Signal processing research includes themes of Underdetermined system, Identifiability and Speech processing.
Karim Abed-Meraim usually deals with Matrix and limits it to topics linked to Array processing and Covariance and Narrowband.
His most cited work include:
- A blind source separation technique using second-order statistics (2268 citations)
- Diagonal algebraic space-time block codes (299 citations)
- Blind system identification (267 citations)
What are the main themes of his work throughout his whole career to date?
Karim Abed-Meraim mainly investigates Algorithm, Blind signal separation, Subspace topology, Signal processing and Speech recognition.
The various areas that Karim Abed-Meraim examines in his Algorithm study include Mathematical optimization, Matrix and Communication channel.
His Matrix research focuses on Minor and how it connects with Orthogonality.
His Blind signal separation research is multidisciplinary, incorporating elements of Independent component analysis, Source separation, Artificial intelligence, Pattern recognition and Iterative method.
The concepts of his Subspace topology study are interwoven with issues in Computational complexity theory, Noise, System identification, Signal subspace and Finite impulse response.
His research investigates the connection with Estimation theory and areas like Control theory which intersect with concerns in Blind equalization.
He most often published in these fields:
- Algorithm (62.79%)
- Blind signal separation (20.93%)
- Subspace topology (20.93%)
What were the highlights of his more recent work (between 2010-2021)?
- Algorithm (62.79%)
- Blind signal separation (20.93%)
- Speech recognition (16.28%)
In recent papers he was focusing on the following fields of study:
Karim Abed-Meraim focuses on Algorithm, Blind signal separation, Speech recognition, Source separation and Beamforming.
His Algorithm study combines topics from a wide range of disciplines, such as Subspace topology, Matrix, Givens rotation, Phase center and Noise.
His Matrix study which covers Deconvolution that intersects with Minification.
His Blind signal separation research is multidisciplinary, incorporating perspectives in Transformation matrix, Filter and Non orthogonal.
His research brings together the fields of Signal and Source separation.
His Beamforming research integrates issues from Transfer function, Reverberation, Computer vision and Artificial intelligence.
Between 2010 and 2021, his most popular works were:
- A view on latest audio steganography techniques (62 citations)
- A New Algorithm for Complex Non-Orthogonal Joint Diagonalization Based on Shear and Givens Rotations (33 citations)
- Unified phase and magnitude speech spectra data hiding algorithm (19 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Telecommunications
Algorithm, Speech recognition, Blind signal separation, Beamforming and Microphone array are his primary areas of study.
In the field of Algorithm, his study on Computational complexity theory overlaps with subjects such as Joins.
Karim Abed-Meraim interconnects Signal-to-noise ratio, Direction finding, Multiple signal classification and Direction of arrival in the investigation of issues within Speech recognition.
His Blind signal separation study incorporates themes from Non orthogonal, Matrix norm, System parameters, Matrix algebra and Orthogonal diagonalization.
His studies deal with areas such as Transfer function, Reverberation, Source separation, Artificial intelligence and Computer vision as well as Beamforming.
His research in Artificial intelligence focuses on subjects like Binaural recording, which are connected to Audio signal processing.
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