What is he best known for?
The fields of study he is best known for:
- Statistics
- Telecommunications
- Algorithm
His scientific interests lie mostly in Algorithm, Gaussian noise, Artificial intelligence, Pattern recognition and Detector.
His research in Algorithm intersects with topics in Control theory and Signal processing.
His Gaussian noise research includes elements of Statistics, White noise, Additive white Gaussian noise and Signal-to-noise ratio.
His work on Speech processing as part of his general Artificial intelligence study is frequently connected to Discrete cosine transform, thereby bridging the divide between different branches of science.
His Pattern recognition study incorporates themes from Clutter, Estimator and Outlier.
The study incorporates disciplines such as Wireless, Channel state information, Fading, Keying and Frequency-shift keying in addition to Detector.
His most cited work include:
- Multiple antenna spectrum sensing in cognitive radios (353 citations)
- Speech probability distribution (251 citations)
- An adaptive KLT approach for speech enhancement (198 citations)
What are the main themes of his work throughout his whole career to date?
Saeed Gazor mostly deals with Algorithm, Detector, Artificial intelligence, Control theory and Pattern recognition.
His work is dedicated to discovering how Algorithm, Additive white Gaussian noise are connected with Phase-shift keying, Speech recognition and Gaussian noise and other disciplines.
His biological study spans a wide range of topics, including Cognitive radio, Detection theory, Electronic engineering and Likelihood-ratio test.
His work carried out in the field of Artificial intelligence brings together such families of science as Noise and Computer vision.
His Control theory study combines topics in areas such as Covariance matrix, MIMO, Signal-to-noise ratio and Communications system.
His Pattern recognition research is multidisciplinary, relying on both Estimator, Statistical model and Maximum a posteriori estimation.
He most often published in these fields:
- Algorithm (41.99%)
- Detector (17.79%)
- Artificial intelligence (15.66%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (41.99%)
- Signal (7.12%)
- Detector (17.79%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Algorithm, Signal, Detector, Hyperspectral imaging and Iterative method.
He has included themes like Cognitive radio, Communication channel and Clutter in his Algorithm study.
He interconnects Transmitter and Relay in the investigation of issues within Communication channel.
His study in Signal is interdisciplinary in nature, drawing from both Electronic engineering, Resolution, Fourier transform and Speech processing.
His Detector research is multidisciplinary, incorporating elements of Phase-shift keying and Likelihood-ratio test.
Part of his project on Hyperspectral imaging includes research on Artificial intelligence and Pattern recognition.
Between 2015 and 2021, his most popular works were:
- Smooth and Sparse Regularization for NMF Hyperspectral Unmixing (24 citations)
- Eigenvalue-Based Multiple Antenna Spectrum Sensing: Higher Order Moments (23 citations)
- ℓ0-Norm Sparse Hyperspectral Unmixing Using Arctan Smoothing (18 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Telecommunications
- Algorithm
His primary areas of investigation include Algorithm, Hyperspectral imaging, Detector, Norm and Likelihood-ratio test.
His Algorithm research incorporates themes from Clutter, Speech recognition, Signal and Mathematical optimization.
Artificial intelligence and Pattern recognition are the two main areas of interest in his Hyperspectral imaging studies.
His studies in Likelihood-ratio test integrate themes in fields like Method of moments, Covariance matrix and Wald test.
Saeed Gazor has researched Covariance matrix in several fields, including Noise and Antenna array.
His Statistics study integrates concerns from other disciplines, such as Asymptotically optimal algorithm and Applied mathematics.
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