Predicting Injury Risk with Machine Learning Methods using a Longitudinal Data Set

Sports injury has long been causing concern to the athlete’s performance, financial aspect, and psychological impact. This is even more prominent in recent years as sports become more widely available o the mass population. Reducing the chances of athletes experiencing injuries not allows them to maintain optimal performance during training and...

Towards an Antarctic meteorite hotspot map

Meteorites contain information on the formation and evolution of the Solar System. Antarctica is the most productive region for collecting meteorites, as the visually contrasting meteorites are easily detectable and tend to concentrate at specific areas exposing blue ice. Blue ice areas act as meteorite stranding surfaces (MSSs) if the flow of...

Stereoscopic Clustered Light Shading

Real-time realistic rendering requires the evaluation of the influence of many light sources. In case of dynamic geometry or light sources, this evaluation must be performed every frame. In this thesis I present enclosed clustering: an adaptation of Clustered Light Shading to stereoscopic rendering which cuts the per-eye cost of light assignment...

Active Semi-Supervised Learning For Diffusions on Graphs

In statistical learning over large data-sets, labeling all points is expensive and time-consuming. Semi-supervised classification allows learning with very few labels. Naturally, selecting a few points to label becomes crucial as the performance relies heavily on the labeled points. The motivation behind active learning is to build an optimal...

Face recognition using traditional machine learning algorithms and deep neural networks with application to face verification

Biometrics authentication has been very useful and necessary nowadays due to the great developments in technology and the transaction of huge amounts of sensitive data on a daily basis. Traditionally, access to some data or service is achieved by means of some documents or a password. However, these methods are not very convenient. Alternatively...

Detecting speech from body movements: A look into the nature of speech based on neural networks and multi-source domain adaptation

Our research focuses on speech detection from body movements using wearable accelerometer data collected in an in-the-wild mingling event. We aim to explore the nature of the connection between speech and body movements. More specifically, we stress on the person-specificity of speech. Many studies have shown that speech always comes along with...

An automated ECG signal quality assessment method with supervised learning algorithm

Wearable health has become a striking area in our daily life.<br/>Electrocardiogram (ECG) is one of the biomedical signals collected by the wearable or portable devices, which is widely used in heart rate monitoring and cardiac diagnosis. However, automatic ECG signal analysis is difficult in real application because the signals are easy to be...

Jump Markov Nonlinear System Identification in Multi-Sensor Target Tracking: A Novel Approach for Multiple Model Joint Tracking and Behavior Classification

The detection of unusual behavior plays a crucial role in the prevention of illegal and harmful activities such as smuggling, piracy, arms trading, human trafficking and illegal immigration. Also for military applications, it is useful to detect anomalous behavior to provide an alert for potential threats, especially with the more recent...

Multidomain Graph Signal Processing: Learning and Sampling

In this era of data deluge, we are overwhelmed with massive volumes of extremely complex datasets. Data generated today is complex because it lacks a clear geometric structure, comes in great volumes, and it often contains information from multiple domains. In this thesis, we address these issues and propose two theoretical frameworks to handle...

Physical Activity Recognition using Wearable Accelerometers in Controlled and Free-Living Environments

Physical activity recognition through wearables has enabled the development of novel applications in healthcare. Most of the existing studies focus on predicting activities using wearable sensors, either in a controlled or uncontrolled environment. However, there is not a clear distinction between these two environments. Hence, this thesis aimed...

Detecting Code Smells in Android Applications

Code smells are patterns in programming code which indicate potential issues with software quality. Previous research resulted in the development of code smell detectors: automated tools which traverse through large quantities of code and return smell detections to software developers.<br/>The Android platform has become very popular over the...

System Building Blocks for Mathematical Operators Using Stochastic Resonance: Application in an Action Potential Detection System

Stochastic resonance (SR) is a phenomenon where the performance of a nonlinear system subjected to noise is better than it is without noise. This phenomenon can be found both in natural and artificial systems, especially in threshold-based systems such as comparators or a population of neurons. As noise always exists and interacts with the...

An Assessment of Cloud Detection Methods Concerning High Altitude Snow and Glacial Environments With Sentinel-2

Glacier fluctuations are regarded as one of the most significant indicators of climate change. The expansion and contraction of glaciers can be observed by outlining glacier boundaries or measuring snow lines with optical Earth observation satellites. New satellites, such as Sentinel-2A/B, provide high spatial resolution images and short revisit...

Features for instrument recognition in polyphonic mixes

Automated instrument recognition is necessary to efficiently obtain instrumentation information for the existing large collections of digital music. While automated instrument recognition is possible with very high accuracy for monophonic fragments, the problem has not yet been solved for polyphonic mixes.In this work a system is designed for...

Elucidating families of ship designs using clustering algorithms

The past decade the packing approach was developed at the TU Delft. This tool automatically generates tens of thousands of ship designs in order to fully explore the design space. This is opposed to the traditional way of ship design, where just a limited number of designs can be elaborated. The result is a set of ship designs that can be used...