What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Operating system
Debasish Ghose mainly focuses on Control theory, Proportional navigation, Glowworm swarm optimization, Mobile robot and Acceleration.
Debasish Ghose has included themes like Simulation and Computer simulation in his Control theory study.
His Proportional navigation study combines topics from a wide range of disciplines, such as Initial value problem, Law and Constant.
Debasish Ghose interconnects Rendezvous and Swarm behaviour in the investigation of issues within Glowworm swarm optimization.
His Mobile robot study is concerned with the larger field of Robot.
His research integrates issues of Missile guidance, Missile, Pursuit guidance, Optimal control and Bounded function in his study of Acceleration.
His most cited work include:
- Scheduling Divisible Loads in Parallel and Distributed Systems (383 citations)
- Obstacle avoidance in a dynamic environment: a collision cone approach (373 citations)
- Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions (301 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Control theory, Mathematical optimization, Artificial intelligence, Proportional navigation and Simulation.
His Control theory study frequently draws connections between related disciplines such as Acceleration.
Debasish Ghose has researched Acceleration in several fields, including Sliding mode control and Computer simulation.
His Mathematical optimization research incorporates themes from Voronoi diagram, Multi-agent system, Convergence and Path.
His biological study deals with issues like Computer vision, which deal with fields such as Obstacle avoidance.
His Proportional navigation research is multidisciplinary, incorporating elements of Autopilot, Law and Missile guidance.
He most often published in these fields:
- Control theory (35.11%)
- Mathematical optimization (17.42%)
- Artificial intelligence (12.92%)
What were the highlights of his more recent work (between 2016-2021)?
- Control theory (35.11%)
- Artificial intelligence (12.92%)
- Computer vision (6.46%)
In recent papers he was focusing on the following fields of study:
Debasish Ghose mainly investigates Control theory, Artificial intelligence, Computer vision, Trajectory and Mathematical optimization.
His work on Control theory as part of general Control theory research is frequently linked to Impact angle, bridging the gap between disciplines.
His Artificial intelligence research integrates issues from Spacecraft, Machine learning and Position.
His study explores the link between Computer vision and topics such as Motion planning that cross with problems in Collision avoidance and Collision.
The various areas that Debasish Ghose examines in his Trajectory study include Kalman filter, Extended Kalman filter, Convergence, Path and Acceleration.
His work on Glowworm swarm optimization is typically connected to Sampling as part of general Mathematical optimization study, connecting several disciplines of science.
Between 2016 and 2021, his most popular works were:
- Collective circular motion in synchronized and balanced formations with second-order rotational dynamics (23 citations)
- Three-dimensional impact angle guidance with coupled engagement dynamics (21 citations)
- Synchronization of multi-agent systems with heterogeneous controllers (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Operating system
Debasish Ghose focuses on Control theory, Artificial intelligence, Control theory, Centroid and Trajectory.
Debasish Ghose merges Control theory with Impact angle in his research.
His work is dedicated to discovering how Artificial intelligence, Computer vision are connected with Obstacle and Inverse and other disciplines.
His Control theory study which covers Bounded function that intersects with Multi-agent system.
His studies in Trajectory integrate themes in fields like Signal strength, Noise, Signal, Path and Pursuit guidance.
His work in Heading covers topics such as Angular velocity which are related to areas like Nonlinear system.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
