What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Statistics
- Artificial intelligence
Robin J. Evans mainly focuses on Control theory, Clutter, Artificial intelligence, Linear system and Computer vision.
His Control theory research focuses on Nonlinear system, Adaptive control, Optimal control, Multidimensional systems and Nonlinear control.
His Clutter research incorporates elements of Tracking system, Tracking, Radar tracker and Sensor fusion.
His Artificial intelligence research includes elements of Radar and Pattern recognition.
His Linear system research includes themes of Probability density function, Information theory, Infimum and supremum, Entropy and Feedback loop.
His Computer vision study combines topics in areas such as Doppler radar, Doppler effect, Track and Constant false alarm rate.
His most cited work include:
- Feedback Control Under Data Rate Constraints: An Overview (852 citations)
- Stabilizability of Stochastic Linear Systems with Finite Feedback Data Rates (598 citations)
- Exponential stabilisability of finite-dimensional linear systems with limited data rates (424 citations)
What are the main themes of his work throughout his whole career to date?
Robin J. Evans focuses on Control theory, Artificial intelligence, Electronic engineering, Algorithm and Mathematical optimization.
His Control theory, Adaptive control, Nonlinear system, Linear system and Control system study are his primary interests in Control theory.
His Control theory study necessitates a more in-depth grasp of Control engineering.
His studies in Artificial intelligence integrate themes in fields like Clutter, Computer vision and Pattern recognition.
His research on Electronic engineering often connects related topics like Electrical engineering.
Robin J. Evans does research in Algorithm, focusing on Estimation theory specifically.
He most often published in these fields:
- Control theory (28.07%)
- Artificial intelligence (12.62%)
- Electronic engineering (11.96%)
What were the highlights of his more recent work (between 2014-2021)?
- Algorithm (11.96%)
- Artificial intelligence (12.62%)
- Radar (5.48%)
In recent papers he was focusing on the following fields of study:
Robin J. Evans spends much of his time researching Algorithm, Artificial intelligence, Radar, Applied mathematics and Graphical model.
His research in Algorithm intersects with topics in Subspace topology, Detector, Markov chain, Monte Carlo method and Hidden Markov model.
His research integrates issues of Cognitive radio and Markov process in his study of Hidden Markov model.
Robin J. Evans combines subjects such as Machine learning, Computer vision and Pattern recognition with his study of Artificial intelligence.
His Radar research is multidisciplinary, relying on both Waveform, Electronic engineering, Convolutional neural network and Gesture recognition.
His Applied mathematics study integrates concerns from other disciplines, such as Latent class model and Conditional independence.
Between 2014 and 2021, his most popular works were:
- Hand-Gesture Recognition Using Two-Antenna Doppler Radar With Deep Convolutional Neural Networks (48 citations)
- Stochastic Geometry Methods for Modeling Automotive Radar Interference (44 citations)
- Graphs for Margins of Bayesian Networks (37 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Statistics
- Artificial intelligence
His scientific interests lie mostly in Markov model, Artificial intelligence, Astrophysics, Gravitational wave and Neutron star.
His Markov model study combines topics from a wide range of disciplines, such as Latent variable model, Latent variable, Bayesian network and Exponential family.
His Latent variable model research is multidisciplinary, incorporating elements of Mathematical optimization and Constraint.
His Mathematical optimization study frequently intersects with other fields, such as Joint probability distribution.
His study in the fields of Hidden Markov model, Adaptive system and Convolutional neural network under the domain of Artificial intelligence overlaps with other disciplines such as Drop.
His work carried out in the field of Hidden Markov model brings together such families of science as Cognitive radio, Algorithm, Machine learning and Markov process.
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