ADR Awareness Centre

Generative Adversarial Networks (GANs) are part of the cutting edge in recent machine learning research.

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.

This lecture overviews Robust Statistics that has many applications in Data Analytics and Digital Signal Processing and Analysis. It covers the following topics in detail: Outliers.

This lecture overviews Robot and Drone Swarms that has many applications in autonomous systems: cars/drones.

This lecture overviews Pedestrian Detection  that has many applications in autonomous car vision and smart city applications.

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

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.

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.

This lecture overviews Video Captioning that has many applications in video description, search and retrieval. It covers the following topics in detail: Video captioning definitions and datasets.

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.

This lecture overviews discrete-time Signals and Systems topics. Discrete-time signals are presented: periodic signals, delta signal, unit step signal, exponential signal, trigonometric signals, complex exponential signal.

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.