What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Miguel Angel Sotelo mainly focuses on Artificial intelligence, Computer vision, Simulation, Poison control and Machine vision.
His studies in Artificial intelligence integrate themes in fields like Pedestrian and Pattern recognition.
His Computer vision study combines topics in areas such as Artificial neural network and Robustness.
His Simulation study integrates concerns from other disciplines, such as Intelligent transportation system, Fuzzy control system, Control engineering, Real-time computing and Global Positioning System.
His Feature extraction research focuses on Object detection and how it relates to Windshield.
Miguel Angel Sotelo works mostly in the field of Sensor fusion, limiting it down to concerns involving Eye movement and, occasionally, Image processing.
His most cited work include:
- Real-time system for monitoring driver vigilance (539 citations)
- Combination of Feature Extraction Methods for SVM Pedestrian Detection (156 citations)
- Adaptive Road Crack Detection System by Pavement Classification (142 citations)
What are the main themes of his work throughout his whole career to date?
Miguel Angel Sotelo mostly deals with Artificial intelligence, Computer vision, Simulation, Intelligent transportation system and Real-time computing.
His biological study deals with issues like Pedestrian, which deal with fields such as Machine learning.
His Computer vision study incorporates themes from Global Positioning System and Robustness.
Miguel Angel Sotelo performs multidisciplinary study on Simulation and Poison control in his works.
His work carried out in the field of Intelligent transportation system brings together such families of science as Control engineering, Control theory, Cruise control and Monocular vision.
His research integrates issues of Classifier and Pedestrian detection in his study of Support vector machine.
He most often published in these fields:
- Artificial intelligence (58.63%)
- Computer vision (45.78%)
- Simulation (14.06%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (58.63%)
- Computer vision (45.78%)
- Deep learning (6.02%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Computer vision, Deep learning, Control theory and Trajectory.
He combines subjects such as Machine learning and Pedestrian with his study of Artificial intelligence.
His Computer vision research incorporates themes from Global Positioning System and Position.
The various areas that he examines in his Global Positioning System study include Real-time computing, Software deployment and Robustness.
His research investigates the link between Deep learning and topics such as Task analysis that cross with problems in Truck.
Miguel Angel Sotelo studied Control theory and Fuzzy logic that intersect with Control engineering, Variable structure control and Sliding mode control.
Between 2017 and 2021, his most popular works were:
- Pedestrian Path, Pose, and Intention Prediction Through Gaussian Process Dynamical Models and Pedestrian Activity Recognition (49 citations)
- A novel sparse representation model for pedestrian abnormal trajectory understanding (34 citations)
- Fault Detection Filter and Controller Co-Design for Unmanned Surface Vehicles Under DoS Attacks (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Control theory, Computer vision, Control system and Robustness.
His Artificial intelligence research integrates issues from Machine learning and Pedestrian.
His work on Kalman filter is typically connected to Water transport as part of general Control theory study, connecting several disciplines of science.
Miguel Angel Sotelo interconnects Artificial neural network, Random forest and Computer simulation in the investigation of issues within Computer vision.
His Control system study also includes
- Damper and Energy consumption most often made with reference to Vehicle dynamics,
- Lyapunov function which connect with Fuzzy logic, Sliding mode control, Variable structure control and Fuzzy control system.
His Robustness research is multidisciplinary, relying on both Simultaneous localization and mapping, Inertial navigation system and Satellite system, GNSS applications, Global Positioning System.
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