Learning Reduced-Order Mappings between Functions: An Investigation of Suitable Inputs and Outputs

Data-driven approaches are a promising new addition to the list of available strategies for solving Partial Differential Equations (PDEs). One such approach, the Principal Component Analysis-based Neural Network PDE solver, can be used to learn a mapping between two function spaces, corresponding to a PDE. However, the practical limitations of...

Learning Reduced Order Mappings of Navier-Stokes: An Investigation of Generalization on the Viscosity Parameter

Solving Partial Differential Equations (PDEs) in engineering such as Navier-Stokes is incredibly computationally expensive and complex. Without analytical solutions, numerical solutions can take ages to simulate at great expense. In order to reduce this cost, neural networks may be used to compute approximations of the solution for use during...

Data Driven Approximations Of PDEs: On Robustness of Reduced Order Mappings between Function Spaces Against Noise

This paper presents a comprehensive exploration of a novel method combining Principal Component Analysis (PCA) and Neural Networks (NN) to efficiently solve Partial Differential Equations (PDEs), a fundamental challenge in modeling a wide range of real-world phenomena. Our research extends the work of Bhattacharya et al. by focusing on PCA for...

CardioSync: Heartbeat-Based BLE Synchronization for Batteryless IoT Devices

Batteryless Internet of Things (IoT) devices powered by energy harvesting enable sustainable and maintenance-free operation, but face challenges in achieving synchronised bidirectional communication between intermittently-powered nodes. This thesis presents CardioSync, a novel framework that leverages the human heartbeat as a shared clock to...

Investigating Effects of Participant Variation on Performance of Visual Stimuli Reconstruction From fMRI Signals Using Machine Learning

Image reconstruction from neural activation data is a field that has been growing in popularity with developments such as neuralink in the brain-machine interface space. To make better decisions when collecting data for this purpose, it is important to know what qualities to optimize for. The present paper investigates the relation between...

Denoising task fMRI data for image reconstructions: Denoising of Functional Magnetic Resonance Imaging (fMRI) Data for Improved Visual Stimulus Reconstruction using Machine Learning

This study aims to investigate the impact of various denoising algorithms on the quality of visual stimulus reconstructions based on functional magnetic resonance imaging (fMRI) data. While fMRI provides a valuable, noninvasive method for assessing brain activity, the reliability of this data can be impaired by multiple noise types, including...

A study of the impact of CNN architecture variation on predicting brain activity using feature-weighted receptive fields

This study investigates the relationship between deep learning models and the human brain, specifically focusing on the prediction of brain activity in response to static visual stimuli using functional magnetic resonance imaging (fMRI). By leveraging intermediate outputs of pre-trained convolutional neural networks (CNNs) with feature-weighted...

Identification of subjects from reconstructed images: Identification of individual subjects based on image reconstructions generated from fMRI brain scans

Reconstructing seen images from functional magnetic resonance imaging (fMRI) brain scans has been a growing topic of interest in the field of neuroscience, fostered by innovation in machine learning and AI. This paper investigates the possible presence of personal features allowing the identification of subjects from their reconstructed images....

Visualization of Fiber Tract Uncertainty during Neurosurgery

In modern neurosurgical practice, a surgeon can see a patient’s fiber tracts (nerve tracts) on a monitor in the operating room. This design study investigates the benefit of adding the uncertainty of the tracts and aims to improve the surgeon’s orientation while reducing visual clutter.<br/><br/>Based on an interview with a neurosurgeon and our...

BladeSynth: Damage Detection and Assessment in Aircraft Engines with Synthetic Data

Deep learning has been widely implemented in industrial inspection, such as damage detection from images. However, training deep networks requires massive data, which is hard to collect and laborious to annotate, especially in the aviation scenario of aircraft engines. To alleviate the demand for annotated data, we create BladeSynth - a large...

Search by Image: Deep Learning Based Image Visual Feature Extraction

In recent years, the expansion of the Internet has brought an explosion of visual information, including social media, medical photographs, and digital history. This massive amount of visual content generation and sharing presents new challenges, especially when searching for similar information in databases —— Content-Based Image Retrieval ...

Assisting agent’s effect on the trustworthiness of their human teammate

Collaborative AI (CAI) is a fast growing field of study. Cooperation between teams composed of humans and artificial intelligence needs to be principled and founded on reciprocal trust. Modelling the trustworthiness of humans is a difficult task because of the ambiguous nature of its definition as well as the effect of team work dynamic. This...

The effects of an agent asking for help on a human's trustworthiness

AI systems have the ability to complete tasks with greater precision and speed than humans, which has led to an increase in their usage. These systems are often grouped with humans in order to take advantage of the unique abilities of both the AI and the human. However, to make this cooperation as efficient as possible, there needs to be a...

Towards Trust in Human-AI Teams

Human-AI teams require trust to operate efficiently and solve certain tasks like search &amp; rescue. Trustworthiness is measured using the ABI model; Ability, Benevolence and Integrity. This research paper tries to observe the effect a conflicting robot has on the human trustworthiness. The hypothesis we try to test is: “human trustworthiness...

Human trustworthiness when collaborating with a friendly agent: Final paper

As technology advances, automated systems become more autonomous which leads to a higher interdependence between machine and human. Much research has been done about trust between humans and trust of humans regarding machines. An interesting question that remains is how the behavior of an agent influences human trustworthiness in a human-agent...

Assessing the Changes in Human Trustworthiness as a Result of an Artificial Agent Directing the Human in Joint Activities

The number of collaborations between humans and artificial agents has risen steeply in recent years due to the rapid expansion of AI. Numerous studies in social sciences have already established that trust is a crucial factor in ensuring effective teamwork. While the dynamics of trust in human-human relationships or the effects of human...

Physics-based sail reconstruction from 3D-markers

To design more efficient sailing boat sails and to analyze the efficiency of a sail trim on the water, it is very helpful to have the ability to obtain a digital copy of real-life sail configurations. As a step towards obtaining such digital copies, the Sailing Innovation Centre in collaboration with GeoDelta has created point cloud measurements...