This lecture overviews Robot and Drone Swarms that has many applications in autonomous systems: cars/drones. It covers the following topics in detail: Definition and Applications, Autonomous car swarms, Communications, Architecture and Formation, Mission Planning, Algorithms, Ant Colony Optimization, PSO, Simulators and metrics.
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Privacy Protection, Ethics and Regulations for Autonomous Systems
This lecture overviews Privacy Protection, Ethics and Regulations for Autonomous Systems that has many applications in Autonomous cars and drones. It covers the following topics in detail: Data Security (Communication disruption, Jamming on components, Manipulation on software, Vehicle hacking), Privacy Protection, Moral Machine, Safety and Regulations, Dual use avoidance.
Self-Awareness in Autonomous Systems
This lecture overviews Self-Awareness that has many applications in in Autonomous Systems and robotics. It covers the following topics in detail: Self-awareness definition, Self-aware systems, Cognitive architecture.
LiDAR in Robotics and Autonomous Systems
This lecture overviews LiDAR principles and technology. Active 3D shape reconstruction methods. Time-of-Flight principle. Pulsed wave. Continuous-wave propagation. Laser ToF Technology: LiDAR. LiDAR in Autonomous Systems: cars, drones. Drone LiDARs (Technical Specifications of Current Model, Alternatives for potential upgrade). Radio wave Technology: Radar. Sonar.
Autonomous Systems Sensors
This lecture overviews Autonomous Systems Sensors that has many applications in Autonomous robots, cars, vessels and drones. It covers the following topics in detail: GPS, RTK-GPS, IMU, RFID Sensors, mono/stereo and event cameras, lidars, inductive/capacitance proximity sensors, Ultrasonic sensors.
Attention and Transformers Networks in Computer Vision
In this lecture focused on Transformers in the field of computer vision, the limitations of Convolutional Neural Networks (CNNs) are emphasized. While CNNs have been the dominant architecture for visual tasks, they face challenges in capturing long-range dependencies and handling variable-sized inputs. However, recent research has shown promising results by combining convolutional layers with attention… Continue reading Attention and Transformers Networks in Computer Vision