Displaying 138 resources
Robot and Drone Swarms
This lecture overviews Robot and Drone Swarms that has many applications in autonomous systems: cars/drones.
Introduction to Multiple Drone Systems
This lecture will provide the general context for this new and emerging topic, presenting the aims of multiple drone systems, focusing on their sensing and perception. Drone mission formalization, planning and control will be overviewed.
Convolutional Neural Networks Lecture
Convolutional Neural Networks form the backbone of current AI revolution and are used in a multitude of classification and regression problems. This lecture overviews the transition from multilayer perceptrons to deep architectures.
Introduction to Cloud/edge computing
Cloud Computing during the time has gained concrete evidence to be a disruptive technology still in its full development. Many drawbacks of the Cloud have brought to improve many their crucial aspects, like performance, security and privacy, etc.
Pedestrian Detection
This lecture overviews Pedestrian Detection that has many applications in autonomous car vision and smart city applications.
Domain Adaptation
This lecture overviews Domain Adaptation that has many applications in DNN training and adaptation.
ECG Signal Analysis
This lecture overviews ECG Signal Analysis as well as other cardiology imaging methods that has many applications in cardiological disorder diagnosis and treatment. It covers the following topics in detail: Background nnowledge of ECG Signals.
Signal Sampling
This lecture overviews Signal Sampling that has many applications in signal acquisition, processing and analysis. It covers the following topics in detail: Discrete/Continuous Signals, Signal Sampling, Signal Reconstruction, Signal Quantization.
Generative Adversarial Networks in Multimedia Content Creation
Deep Convolutional Generative Adversarial Networks (DCGAN) have been used to generate highly compelling pictures or videos, such as manipulated facial animations, interior and outdoor images, videos.
Fast 1D Convolution Algorithms
1D convolutions are extensively used in digital signal processing (filtering/denoising) and analysis (also through CNNs). As their computational complexity is of the order O(N^2), their fast execution is a must.
This lecture will overview
Neural Image Compression
This lecture overviews Neural Image Compression that has many applications in image storage and communications.