What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
His primary areas of study are Algorithm, Artificial intelligence, Compressed sensing, Applied mathematics and Mathematical optimization.
His Algorithm study combines topics in areas such as Spectral density estimation, Fourier transform, Multidimensional systems and Wideband.
In his study, Parametric statistics and Signal processing is strongly linked to Pattern recognition, which falls under the umbrella field of Artificial intelligence.
His biological study spans a wide range of topics, including Data acquisition and Ground-penetrating radar.
James H. McClellan has researched Applied mathematics in several fields, including Linear prediction, Chebyshev iteration, Chebyshev nodes and Nonlinear system.
His research integrates issues of Chebyshev polynomials and Exponential function in his study of Mathematical optimization.
His most cited work include:
- A Compressive Sensing Data Acquisition and Imaging Method for Stepped Frequency GPRs (274 citations)
- A unified approach to the design of optimum FIR linear-phase digital filters (247 citations)
- TOPS: new DOA estimator for wideband signals (199 citations)
What are the main themes of his work throughout his whole career to date?
James H. McClellan mostly deals with Algorithm, Artificial intelligence, Signal processing, Computer vision and Ground-penetrating radar.
His study in Algorithm is interdisciplinary in nature, drawing from both Direction of arrival, Speech recognition, Beamforming, Clutter and Mathematical optimization.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Pattern recognition.
His Signal processing study combines topics from a wide range of disciplines, such as Digital signal processing and Multimedia.
His research investigates the connection between Computer vision and topics such as Radar imaging that intersect with issues in Image resolution.
In his research, Data acquisition is intimately related to Compressed sensing, which falls under the overarching field of Ground-penetrating radar.
He most often published in these fields:
- Algorithm (33.73%)
- Artificial intelligence (18.82%)
- Signal processing (16.08%)
What were the highlights of his more recent work (between 2013-2021)?
- Algorithm (33.73%)
- Electromagnetic induction (6.67%)
- Artificial intelligence (18.82%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Algorithm, Electromagnetic induction, Artificial intelligence, Amplitude and Microseism.
James H. McClellan is interested in Least squares, which is a field of Algorithm.
His Artificial intelligence study incorporates themes from Fast Fourier transform, Ground-penetrating radar, Parametric model and Pattern recognition.
James H. McClellan combines subjects such as Fourier series and Data compression with his study of Amplitude.
He interconnects Passive seismic, Time–frequency representation, Event and Noise in the investigation of issues within Microseism.
His studies in Acoustics integrate themes in fields like Telecommunications, Broadband and Tone.
Between 2013 and 2021, his most popular works were:
- Seismic data denoising through multiscale and sparsity-promoting dictionary learning (64 citations)
- Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7.9 Wenchuan Earthquake (23 citations)
- Efficient Algorithm Design for GPR Imaging of Landmines (20 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
James H. McClellan spends much of his time researching Algorithm, Artificial intelligence, Pattern recognition, Microseism and Signal processing.
His Algorithm research integrates issues from Convolution, Beamforming and Direction of arrival.
As part of one scientific family, James H. McClellan deals mainly with the area of Convolution, narrowing it down to issues related to the Energy, and often Mathematical optimization.
The study incorporates disciplines such as Aftershock, Phase detector, Computer vision and Radar in addition to Artificial intelligence.
His work deals with themes such as Deep learning and Matched filter, which intersect with Pattern recognition.
His work deals with themes such as Noise measurement, Sparse approximation, Data structure and Noise, which intersect with Signal processing.
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