Brain-inspired feature extraction for near sensor extreme edge processing with Spiking Neural Networks

Motivated by the desire to bring intelligent processing at the Edge, enabling online learning on resource- and latency-constrained embedded devices has become increasingly appealing, as it has the potential to tackle a wide range of challenges: on the one hand, it can deal with on-the-fly adaptation to fast sensor-generated streams of data under...

Assessing the Efficacy of Deep Learning Models in the Context of Active Flow Control

In the field of fluid mechanics, there has been a significant shift towards the integration of machine and deep learning techniques to address challenges in reduced-order modeling, flow feature analysis, and control, especially within the realm of active flow control (AFC) for objectives such as lift optimization and drag reduction. Deep...

Mining massive open online courses (MOOCs) for software testing knowledge

Software testing is a necessary aspect of software development. With high expectations placed on software testers and a shortage of qualified professionals, Massive Open Online Courses (MOOCs) have emerged as a potential solution to improve software testing education. MOOCs provide accessible education and can offer a comprehensive review of...

Reinforcement Learning for Quadcopter Fault Tolerance: Analyzing Hierarchical and Online Deep Reinforcement Learning for Quadcopter Fault Tolerant Control

The demand of adding fault tolerance to quadcopter control systems has significantly increased with the rise of adoption of UAVs in numerous sectors. This work proposes and demonstrates the use of Hierarchical Reinforcement Learning to control a quadcopter subject to severe actuator fault. State-of-the-art algorithms are implemented, and a...

Online reinforcement learning with sparse rewards through an active inference capsule

Intelligent agents must pursue their goals in complex environments with partial information and often limited computational capacity. Reinforcement learning methods have achieved great success by creating agents that optimize engineered reward functions, but which often struggle to learn in sparse-reward environments, generally require many...

Teleoperated online Learning from Demonstration in a partly unknown environment: using a semi-autonomous care robot

The general approach to generate collision free motion in a constraint environment is to use path planners, which demand a known environment and potentially fail otherwise. Learning from Demonstration (LfD) can be used instead to teach the robot unknown parts of the environment, such as a goal deviation or an unforeseen obstacle. The general...

Online Agent-Based Aerial Patrol Planning for Wildlife Surveillance

Wildlife conservation efforts are constrained by a limited amount of resources available for surveillance activities. UAVs are used increasingly to assist rangers in patrol tasks. Effectively patrolling wildlife parks requires detailed knowledge of the environment and its threats, which is not always available. Previous work in Green Security...

Online Convex Optimization with Predictions: Static and Dynamic Environments

In this thesis, we study Online Convex Optimization algorithms that exploit predictive and/or dynamical information about a problem instance. These features are inspired by recent developments in the Online Mirror Decent literature. When the Player's performance is compared with the best fixed decision in hindsight, we show that it is possible...

Online Learning of Tire Behaviour combined with MPC for Autonomous Racing

In this project, a unique method of combining online learning with model predictive control is applied to autonomous racing. A concern in autonomous racing is that accurate models that encapsulate the dynamics of the vehicle are complex, nonlinear, and difficult to identify. In order to make this more practical for control purposes, the...

Online Reinforcement Learning for Flight Control: An Adaptive Critic Design without prior model knowledge

Online Reinforcement Learning is a possible solution for adaptive nonlinear flight control. In this research an Adaptive Critic Design (ACD) based on Dual Heuristic Dynamic Programming (DHP) is developed and implemented on a simulated Cessna Citation 550 aircraft. Using an online identified system model approximation, the method is independent...

Reinforcement Learning for Flight Control: Learning to Fly the PH-LAB

In recent years Adaptive Critic Designs (ACDs) have been applied to adaptive flight control of uncertain, nonlinear systems. However, these algorithms often rely on representative models as they require an offline training stage. Therefore, they have limited applicability to a system for which no accurate system model is available, nor readily...

Scaling Up Closed Loop Plastics Recycling

Over the course of the 20th century, plastics have transformed the way we use, design and manufacture the products that define our everyday lives. Yet as our global demand for plastics increases, so does the amount of plastic waste that is created. Currently only a small amount of the material value of this waste stream is retrieved through...

E-learning portal for home care planning

This master graduation project is focused on designing an E-learning portal, for home-care managers and planners, to learn working with Careware. Careware is an hour registration program (accessed by web-portal) from helpLine B.V. that provides IT support for care providers. It enables caregivers to access more functionalities and data in...