What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Programming language
His primary areas of investigation include Artificial intelligence, Robot, Simulation, Computer vision and Human–computer interaction.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Context, Machine learning and Field.
His research investigates the connection between Robot and topics such as Task that intersect with issues in Trajectory, Robot kinematics and Control theory.
Alois Knoll has included themes like Electric vehicle, Traffic simulation, Robotic surgery and Articulated robot in his Simulation study.
His Computer vision research focuses on subjects like Industrial robot, which are linked to Perception.
His Human–computer interaction research incorporates themes from Situated, Benchmark, Natural language and Social robot.
His most cited work include:
- Gradient boosting machines, a tutorial. (489 citations)
- A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks (148 citations)
- The Human Brain Project: Creating a European Research Infrastructure to Decode the Human Brain (126 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Robot, Computer vision, Simulation and Control engineering.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Task and Pattern recognition.
The various areas that Alois Knoll examines in his Robot study include Control theory and Human–computer interaction.
Computer vision is often connected to Robustness in his work.
Alois Knoll specializes in Simulation, namely Haptic technology.
His primary area of study in Fuzzy logic is in the field of Fuzzy control system.
He most often published in these fields:
- Artificial intelligence (34.64%)
- Robot (20.32%)
- Computer vision (16.20%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (34.64%)
- Robot (20.32%)
- Computer vision (16.20%)
In recent papers he was focusing on the following fields of study:
Alois Knoll focuses on Artificial intelligence, Robot, Computer vision, Artificial neural network and Reinforcement learning.
His work in Artificial intelligence addresses issues such as Machine learning, which are connected to fields such as Task.
His work carried out in the field of Robot brings together such families of science as End-to-end principle, Control engineering, Control theory and Robotic arm.
Control theory is a subfield of Control theory that Alois Knoll tackles.
His Computer vision study combines topics from a wide range of disciplines, such as Frame and Optical coherence tomography.
The study incorporates disciplines such as Model predictive control and Trajectory in addition to Artificial neural network.
Between 2017 and 2021, his most popular works were:
- A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks. (50 citations)
- Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results. (41 citations)
- OPC UA versus ROS, DDS, and MQTT: Performance Evaluation of Industry 4.0 Protocols (41 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Programming language
Alois Knoll spends much of his time researching Artificial intelligence, Robot, Computer vision, Neuromorphic engineering and Reinforcement learning.
His Artificial intelligence research includes elements of Field and Machine learning.
Alois Knoll has researched Robot in several fields, including Orientation, Tracking, Process and Software.
His biological study spans a wide range of topics, including Frame, Microsurgery, Convolutional neural network and Optical coherence tomography.
In his study, Hyperparameter optimization, Parameterized complexity, Degrees of freedom and Gait is inextricably linked to Control theory, which falls within the broad field of Reinforcement learning.
His Artificial neural network research incorporates elements of Generalization, Trajectory and Mobile robot.
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