The measurement of the heart rate (HR) is of vitalimportance in modern medicine. Advancements in medical technology have resulted in a myriad of techniques to measure and analyze these bio-signals, and the advent of telemedicine and the post-COVID-19 world has placed greater emphasis on contact-free measurement tools.

Previous works have explored several methods of contact-free heart rate measurement. Remote Photo-Plethysmography(rPPG) measures HR from RGB camera streams by detecting and analyzing the frequency of “micro-blushes” in the skin corresponding to pulse; this method can also be used to estimate SpO2. Eulerian video magnification instead attempts to detectsubtle micro-movements caused by the pulse to measure HR aswell as RR. Ballistocardiography tracks longitudinal movements of feature points to estimate HR. However, there is little research on applying these (typically computationally-intensive) algorithms in the context of real-time, low-performance embedded systems.

This paper evaluates the computational requirements of algorithms used in extracting bio-signals using an RGB camera. It focuses on rPPG, a camera based method for extracting heart rate from video. Several rPPG implementations are tested on standard hardware and benchmarked on real-time performance and memory. The experiments were conducted on publicly available datasets. Additionally, region-of-interest selection algorithms are also compared. These results are of much use in developing embedded devices for remote monitoring of bio-signals and provide some insight into algorithms viable for use in real-time contexts.

Heart rate (HR) is a critical indicator of an individual’s health, serving as a key metric for detecting potential cardiac issues. This paper explores a method for real-time heart rate measurement using RGB camera footage, aimed at general health monitoring. The proposed method utilizes a convolutional neural network (CNN) to generate a 3D mesh of the subjects’ facial features. The movement over time of the points in this mesh is used to compute a signal that captures the small pulsatile movements corresponding to the mechanical motion of blood being pumped through the veins. This signal is filtered, and motion sources are separated using principal component analysis (PCA). The most periodic component, the one with the highest frequency, is considered to correspond to the heart rate, and it’s frequency is used to estimate the heart rate. The proposed method is tested using the ECG-Fitness dataset, characterized by challenging environmental conditions such as significant subject motion and dim lighting conditions. Experimental results demonstrate the method’s capability for real-time applications, though further enhancements are needed to improve robustness under difficult environmental conditions.

Respiratory Rate (RR) is a vital health indicator, especially in infant monitoring, where early detection of abnormalities or variabilities in RR is crucial. Traditionally, the respiratory rate is extracted using contact-based methods, which, although reliable, can be quite intrusive and stressful for long-term monitoring. This study explores the potential of real-time remote RR monitoring on inexpensive hardware, by comparing three motion-based methods of extracting RR from RGB-camera feed: Pixel Intensity Changes (PIC), Optical Flow (OF), and Eulerian Video Magnification (EVM). The three algorithms were benchmarked using the public AIR-125 dataset, which features videos of infants in various positions, with a focus on their accuracy and computational intensity. The results show that the PIC algorithm slightly outperformed the other two algorithms in both accuracy and computational complexity. However, none of the algorithms managed to replicate the performance of the study which initially proposed the dataset as a benchmark.

A reliable dependency resolution process should minimize dependency-related issues. We identify transparency, stability, and flexibility as the three core properties that define a reliable resolution process and discuss how different dependency declaration strategies affect them. To increase the reliability of Maven's dependency resolution we identify two patterns of misuse, or smells, that commonly occur in Maven projects: the presence of used undeclared dependencies and conflicting soft version constraints. We introduce and evaluate a proof-of-concept method, MaRCo, designed to address these smells. MaRCo increases transparency by injecting used undeclared dependencies and balances stability and flexibility by replacing soft version constraints with compatible version ranges. The version ranges are generated through a dependency-specific approach to compatibility using bytecode differencing and cross-version testing. The empirical evaluation of MaRCo shows that while the ranges generated by the dependency-specific approach may be stricter than necessary, they are unlikely to contain breaking changes. Overall, we see that MaRCo is able to make the resolution process slightly more reliable, affecting 13% of dependencies in 71% of projects, in a way that is more stable than a soft constraint-only approach, and more flexible than a hard constraint-only approach.

Summarising patient interactions creates a huge workload for the healthcare professionals. This research finds that patient interactions contain a lot of noise that is subjective of nature. To explore the problem area interviews with a summarisation prototype have been conducted to extract system requirements and validate those by implementing them in the prototype. Filtering noise, reinforcement learning and numerical factual correctness are of paramount importance to a successful summarisation system.

The Shunt Routing Problem (SRP) is an important logistic problem at nearly every railway station. It is a subproblem of an even bigger train scheduling problem, the Train Unit Shunting Problem (TUSP). The objective of SRP is to schedule conflict-free routes between the platforms and the yards, for currently non-operational trains that have completed their current journeys and have not yet commenced the next one.
We developed a Constraint Programming (CP) model for the Shunt Routing Problem at NS. The model is intended to replace the current algorithm for constructing the initial solution for the Local Search, addressing the TUSP at NS. The model incorporates all essential feasibility requirements and produces conflict-free solutions, in contrast to its forerunner. Moreover, it can be applied to any instance and station layout.

Initially, the model exhibited an average performance of 7-8 minutes on a real-life instance at Enkhuizen station. We managed to enhance the model and reduce the computation time to less than a second. The key improvements were achieved by changing the solver search strategy, imposing additional search guidelines, or further restricting the domains of influential variables. During experimentation, an optimized second version of the model was proposed.
The evaluation undoubtedly confirmed that it outperforms the first version.

Both versions of the model, without any additional enhancements, perform exceptionally well on a second real-life instance at Amersfoort station, solving it in milliseconds. Even with a considerable number of additional routes, the performance of the second version remained reasonable, with a computation time of 18 seconds.

A final experiment revealed the remarkable performance of Gecode, achieving computation time in milliseconds with the second version of the model implemented for Enkhuizen in MiniZinc. This performance is an order of magnitude faster compared to the default performance of Google OR-Tools on Enkhuizen with the second version.

Ever since the origin of human life, we have been infected by a wide range of viruses. These pathogens have invaded our cells, leaving behind traces of their presence in our genome, known as endogenous viral elements (EVEs). Among the affected cells are neurons. The infectious hypothesis for Alzheimer's disease (AD) proposes that viral infections may serve as an environmental factor contributing to AD. In our study, we explored this hypothesis for the first time from an endogenous perspective by identifying EVEs in the genomes of both AD patients and cognitively healthy centenarians (CHCs). Using a custom-built data processing pipeline, our findings reveal that the genomes of AD patients on average harbor more EVEs than those of CHCs (p=3.24e-4, incidence rate ratio (IRR) = 1.27). Furthermore, we identified specific chromosomal regions with a higher incidence of viral integration in the AD cohort across different virus families. Our data suggest that viral infections over time have increased the susceptibility to AD, underscoring the importance of preventive measures against infections.

For my master’s thesis, I developed a novel indoor localization approach that can achieve accurate localization results. Indoor localization is a well-known topic of research, and many attempts have been made to find the so-called holy grail. For a robotic car swarm to be able to execute a specific task in a specific room, each robot in the swarm needs to know its own location with respect to a map of the building. Allowing the robot to deduce this location himself allows for total automation of the swarm and helps mitigate errors along the way due to faulty sensor readings. Numerous works have tried to solve this problem by using beacons or heavily trained machine learning networks. These methods, however, prevent the robot from working everywhere, as either the location needs to be adapted or the robot needs to be trained for the location. To mitigate these issues, the work of this thesis is focused on achieving indoor localization that works everywhere by researching if a combination of a particle filter and sound source localization techniques can achieve high-accuracy indoor localization.

To achieve this, particle filter and source localization techniques are combined based on probability theory. Here, so-called localization tables are used to estimate the robot’s position based on the geo- metrical properties of the map, the DOA, and the distance from a received message. This table is then used to update the position and weight of the particles, which can then be used to make an educated guess of the robot’s position. This approach proved to be effective, as it was able to achieve average RMSE results as low as 3.83 cm, where the robot only needed to drive 363 cm and localization was achieved within 9.52 seconds. The research showed that deploying more cars in the swarm leads to better results, as fewer transmissions are needed, and less distance needs to be traveled. In addition, the research also showed that localization can be achieved while driving even less distance by sharing the localization tables between the robots. This, however, does come at the cost of localization accu- racy, resulting in an RMSE of 19.89 cm while driving only 158 cm. To conclude this research, based on the results of the novel fusion of a particle filter and sound source localization techniques, it can be concluded that the work in this thesis offers a great contribution to the field of indoor localization using audio-based signals.

As machine learning algorithms become more and more prevalent, so do the inherent risks of unfair classification of disadvantaged or underrepresented groups. Additionally, in a dynamic context, the underlying distributions can shift over time, so corrective measures that can work in a static context may end up being detrimental in the long run. In this paper, we propose a new algorithm with the aim to improve fairness in datasets, by modifying the commonly used SMOTE algorithm in a way to work better in a dynamic context, with an added focus on fairness criteria. The results in this paper indicate that this modification, labelled as the SMICT algorithm, can be a promising approach to improving fairness, albeit with limitations and challenges that need to be considered whenever the algorithm is used.

Fairness in machine learning is an increasingly important yet complex issue, especially as these algorithms are integrated into critical decision-making processes across various sec- tors. This research focuses on the impact of features under fairness properties across multiple sectors. The primary research question ad- dressed is: “Which data features are the most sensitive when monitoring fairness properties on criminal data, and how do these features perform when monitoring fairness properties on data from different sectors?” The study ex- amines features such as age, race, gender, and educational level across datasets from criminal justice, healthcare, finance, and education sectors. Utilizing logistic regression models and a proposed dynamic monitoring algorithm, sensitivity of features to fairness violations is assessed for Demographic Parity and Equal Opportunity properties. The findings indicate that age is the most sensitive feature in almost all sectors, highlighting inherent biases and the necessity for sector-specific fair- ness considerations. However, statistical analysis revealed that these differences in sensitivity values across sectors were not statistically significant, suggesting that the observed pat- terns are not strong enough to be deemed conclusive.

Recent scandals like the dutch Toeslagenaffaire have shown the importance of fairness monitoring of machine learning models. When not careful, automated decision making models can unfairly favor groups of people and discriminate other groups. The results can be devastating for the people involved. It has been recognised that this problem requires proper research. However, most of the already conducted research looks at the problem in a static context, while almost all the real life applications are a dynamic process. Datasets are constantly increasing, and an automated decision process can have an effect on the newer entries on this dataset. A new problem then arises, when looking at these prediction tasks in a dynamic context, is the older data just as relevant as the new entries? This question can be answered by the use of fading algorithms. Fading algorithms use different methods to prioritise new data and forget old data. This paper investigates the effect of these fading algorithms on the fairness of a model. The different methods researched are an abrupt fading algorithm, a gradual fading of weight algorithm and a gradual fading of amount of data algorithm. This research resulted in the showing of importance of looking at the data in a dynamic context, observing a significant improvement on the equality of opportunity, at the cost of the efficiency of the model.

A curve is a continuously bending line with no angles that can be found anywhere in the real world, forming shapes and outlines. They are also the building blocks of historic watermarks, imprinted images on paper that may be used to identify its manufacturers. Their shapes consist of curves as bent wires are used in their production process. Often, the processing of scans of those curves may introduce gaps or a degraded quality which could be corrected by reconstructing the curves in those gaps. Curve reconstruction is a fundamental problem with many research applications, one of which is the reconstruction of curves for binarised scans of historic watermarks. In this paper, a data generation approach is proposed for the simulation of the watermark curves domain through singular automatically generated curves and human-drawn sketches which are then used along binarised watermark scans. I propose a hybrid method combining machine-learning and analytical approaches for curve reconstruction, aiming to leverage their advantages together. The method is compared to its components separately. Quantitative results against them demonstrate the superiority of the pure machine learning approach, as well as the need for more research into potentially better analytical components and a more realistic domain simulation.

Feature extraction techniques for content-based image retrieval are explored, focusing on black-and-white images in the context of historical watermarks. Orthogonal moments and texture features are found to be most applicable. Seven methods are evaluated: four different orthogonal moments, Gabor features, and two novel combinations of orthogonal moments with Gabor features. Retrieval effectiveness is judged based on the precision-recall curve and mean average precision, and watermarks are considered when unchanged, rotated, sheared and both rotated and sheared. The results demonstrate that research into improving efficient grayscale image representation does not translate over to improvements with black-and-white images. As it stands, the basic Zernike moments and novel Gabor-Zernike features are most effective.

A watermark image is a scan of a historical paper document that contains a watermark, which is a motif embedded in the paper that provides valuable information on the origins of a document. Developing tools to automatically identify watermarks can make this information more accessible to researchers. This paper focuses on one specific binarization technique, thresholding. Thresholding selects a threshold value, which is used to turn an image binary such that one color represents foreground and the other represents background. Ideally, binarization isolates the watermark’s shape by representing it as foreground, and removes unwanted information. This research compares the effectiveness of different thresholding techniques when applied to watermark images. Eight algorithms are selected from the literature, and a novel algorithm is proposed that seeks to improve on the other algorithms when applied to watermarks. The nine total algorithms are evaluated quantitatively on synthetic data, and qualitatively through a survey where participants select which algorithm appears best and rate it. The results show that there is no clear algorithm which works best for all images, however a logical adaptive approach may work marginally better than other approaches. Additionally, the presented algorithms do not adequately remove non-watermark information from the images. Further research should be conducted to analyze different binarization techniques in this context.

This paper tackles the problem of evaluating the task of finding similar scanned historical watermarks - small images embedded in historical paper that have been digitized to be processed on a computer - using pre-trained neural networks. This research aims to identify an efficient and accurate alternative to the traditional, time-consuming manual detection methods for finding similar watermarks. The primary issue addressed is the inefficiency of these manual methods. The evaluation focuses on finding similar watermarks for a specific query watermark, assessing the efficacy of neural networks in comparison to a prior art system that employs traditional image processing techniques. This comparison aims to determine how well these neural networks perform in the task of watermark similarity detection. The study involves a dataset of 500 labeled images tested in two distinct contexts: one using unprocessed images and another using images processed to keep only the watermark outline. The results show that pre-trained models achieve higher accuracy and time efficiency compared to the prior art system that uses image processing. These models demonstrate significant effectiveness in watermark recognition and comparison, with each network achieving over 80% accuracy for traced watermarks. EfficientNetB0 achieved 94.66%, VGG16 89.33%, ResNet50 86.67%, and InceptionV3 84%, while the prior art system gets 64,8%. These results conclude that these models are valuable tools in the field of watermark recognition and comparison.

Watermarks have an essential role in identifying the origins and age of specific documents. However, this is often a laborious process. One of the main issues in automatic watermark segmentation is the presence of text that obstructs it, making it difficult to properly reconstruct a watermark. Image processing and machine learning techniques face limitations, requiring time, training of data, or manual parameter selection. This research introduces a new method using wavelets transform to locate and remove text from a watermarked image, while preserving the underlying watermark. This method manages to outperform classic image processing techniques for the case when text is thicker than the watermark outline.

Optimizing SQL query execution through effective cost models is a critical challenge in database management systems (DBMS). This thesis introduces a modular benchmarking system for cost models, with a pluggable architecture for both cost models and execution engines, enabling comprehensive benchmarking across various scenarios. Accompanied by a detailed methodology for the empirical measurement of cost model performance across different execution engines, a standardized approach is established, ensuring consistent and reproducible benchmarks. Furthermore, as a showcase of the developed system's capabilities, an analysis of key features influencing join-order optimization performance in both CPU and GPU systems is presented. This analysis demonstrates the system's utility in developing more effective cost models and optimizers. These contributions pave the way for future research in DBMS optimization, providing a research platform for the accelerated development of new cost models.

Scenario-based learning uses interactive scenarios to present the user with situations that need user input to be resolved in order to teach the user the correct behaviour. Traditionally these scenarios would be presented in a rigid order that is linearly increasing in difficulty. Every student however has a different learning rate. Studies have shown that students can lose motivation when challenged too much or too little. This project aims to improve learning efficiency and user satisfaction by adapting the scenario content to the user's skill and knowledge level.
A driving instructor application was developed where users are presented with short interactive scenarios in a 3D environment where they take control of the vehicle using a steering wheel and pedals and learn to make correct decisions when driving. For the research experiment the sessions were divided into two different modes, linear or adaptive. In the linear mode there was a strict gradual increase in difficulty. In the adaptive mode, the user's performance in the previous scenarios determined which scenario is presented next. The collected on user performance and self-efficacy showed no significant difference in user performance. There was however a significant increasement in self-efficacy when users played the adaptive session. This validates the positive value that personalization could bring to training applications.

Immersive technologies, such as head-mounted devices for Virtual Reality (VR), have experienced increased availability on the consumer market. This opens up numerous possibilities for scientific research and industrial applications. But, configuring and combining data from various sources to create an effective visualization to explore, collaborate in and interact with, has only been researched in limited aspects. This thesis aims to demonstrate the feasibility of integrating these diverse aspects into a single application while preserving their individual potential.

To achieve this objective, an extensive analysis of the literature was done to establish the state of the art. All aspects necessary to create a single application were investigated to define how much of the literature fully or partly implemented them. Findings of the literature were summarized through definition, progress, limitations and improvements.

Based on the analysis, a proof of concept was designed and implemented, which established a virtual reality framework for real-time analysis and exploration of heterogeneous urban data. The framework involved the development of both a client and server applications with real-time communication. The server application processes and caches input data based on a structured configuration file, while the client application renders and visualizes points and shapes based on instructions from the server. The layered processing approach in this proof of concept facilitated real-time data handling and exploration. Which also makes it possible to collaborate with multiple people working on or manipulating the same data at the same time.

Evaluation of the server application showed its capability of processing over 1.000.000 objects from GeoJSON data. After initial rendering, the client application could update over 4.000 objects with a latency of just 200ms, which is considered to be interactive. Feedback from experts further supported the validation of the proof of concept application as a viable solution to the research question.

This research established the validity of a configurable real-time analysis and exploration solution in virtual reality. With further development and research, this tool holds potential for being used in diverse environments dealing with urban data.

The ever-increasing demand for computing has led to the need for specialized heterogeneous hardware, and the frameworks required to utilize them. Besides the traditional central processing units, more and more programs will make use of specialized hardware to accelerate computations. However, the increase in computing also leads to shorter mean time between failures. In this thesis, we apply fault tolerance to Portable Computing Language (PoCL), an open-source implementation of the OpenCL standard. We show that our solution is easy to apply to existing programs making use of PoCL/OpenCL and is able to greatly reduce the total number of errors visible to the end user. Our solution can be used on any device supported by PoCL and provides a low overhead, given that the hardware requirements are met.