Analyzing the Neural Dynamics of Emotional Associative Memory Encoding through Data-Driven Modeling

Researchers have been interested in studying the connection between emotion and memory for decades but much remains unknown due to the elusive nature of the human brain. Furthering our understanding of the phenomenon is crucial for improving the treatment of neurological disorders associated with emotion dysregulation, as well as for enhancing...

Enhancing Deep Networks through Customized Iterative Hierarchical Data Augmentation: A Study utilizing the Sussex-Huawei-Locomotion Dataset

Artificial Neural Networks (ANNs) have emerged as a powerful tool for classification tasks due to their ability to outperform traditional methods. Nevertheless, their effectiveness relies heavily on the availability of large, varied, and labeled datasets, which are often not available. To counter this constraint, data augmentation techniques...

On Game-Theoretic Planning with Unknown Opponents' Objectives

Many autonomous navigation tasks require mobile robots to operate in dynamic environments involving interactions between agents. Developing interaction-aware motion planning algorithms that enable safe and intelligent interactions remains challenging. Dynamic game theory renders a powerful mathematical framework to model these interactions...

Linear State-Space System Identification for Automatic Greenhouse Climate Control

Over nine billion people will have to be fed fresh and healthy food in 2050 according to United Nations. This puts pressure on the horticulture sector, responsible for a large portion of the world’s food production. The literature has shown that automatic optimal control algorithms are able to make better use of resources and can even outperform...

Continuously learning where to go next from observing pedestrians

Socially compliant robot navigation in pedestrian environments remains challenging owing to uncertainty in human behavior and varying pedestrian preferences in different social contexts. Local optimization planners like Model Predictive Control can incorporate collision avoidance constraints, but they can only lead to socially compliant...

Modeling and scheduling an autonomous sorting system using a switching max-plus linear model

Sorting systems form an example of event driven systems. These types of systems are referred to as discrete event systems (DES), and they consist of jobs that need to be performed at available resources. In an autonomous sorting system, jobs consist of robots receiving and delivering parcels at the correct locations. With scheduling, optimal...

Hierarchical Data-enabled predictive control: With application to greenhouse tomato crop production

With the rapidly growing world population and improving living standards of upcoming economies, the demand for fresh food is increasing vastly. Greenhouses have proven to be very effective in increasing crop yield since they offer a sheltered environment that can be controlled. Greenhouse growers need to take care of many processes, among which...

Automatic Controller Selection on a Humanoid Robot: Using Optimization Techniques

Designing controllers for complex robots is not an easy task. Often, researchers hand-tune controllers for humanoid robots, but this is a time-consuming approach that yields a single controller which cannot generalize well to varied tasks. This thesis presents a method which uses the NSGA-II multi-objective optimization (MOO) algorithm with...

Emergency Escape Manoeuvre of a Faulty Truck in a Platoon Formation Moving in Highway

The platooning of trucks provides significant benefits in the existing transportation systems regarding social, economic and environmental aspects. Due to the platoon's high dependence on sensors, it is critically important to have fault-tolerant countermeasures that deal efficiently with failures by achieving minimal risk condition. This thesis...

Formal Synthesis of Optimal Neural Network Controllers

Stability, safety and optimality are often sought-after properties in the field of controller synthesis. In the last century, linear control theory has matured to a level where scalable algorithms are widely available that are able to synthesize controllers with stability and optimality guarantee. However, the synthesis of safe controllers...