Image watermarking for Machine Learning datasets: Using SVD based image watermarking techniques to watermark numerical ML datasets

The media watermarking technique domain has had the last 30 years to develop itself. The non-media side, however, is a way newer sub-domain. [1] The data-gathering process for machine learning algorithms is a tedious and time consuming task. This becomes worse as the scale of these algorithms increases. Thus, protecting the datasets against...

Tensor decomposition for Independent Component Analysis: Through implicit cumulant tensor manipulation

Blind Source Separation (BSS), the separation of latent source components from observed mixtures, is relevant to many fields of expertise such as neuro-imaging, economics and machine learning. Reliable estimates of the sources can be obtained through diagonalization of the cumulant tensor, i.e., a fourth-order symmetric multi-linear array...

An enhanced algorithm for online Proper Orthogonal Decomposition and its parallelization for unsteady simulations

Proper Orthogonal Decomposition (POD) plays an important role in the analysis of complex nonlinear systems governed by partial differential equations (PDEs), since it can describe the full-order system in a simplified but representative way using a handful of dominant dynamic modes. However, determining a POD from the results of complex...

Multiparametric ultrasound and machine learning for prostate cancer localization

A cost-effective, widely available, and practical diagnostic imaging tool for prostate cancer (PCa) localization is still lacking. Recently, the contrast-ultrasound dispersion imaging (CUDI) technique has been developed for PCa localization by quantifying dynamic contrast-enhanced ultrasound (DCE-US) acquisitions. Tissue stiffness is an...

Analyzing Functional Ultra- sound Images of the Brain Using Tensor Decompositions

Functional ultrasound (fUS) is a neuroimaging modality that offers high spatial and temporal resolution while also providing portability. In this thesis, neuroimaging data acquired with fUS at Center for Ultrasound and Brain imaging at Erasmus MC (CUBE) is processed. Due to the fact that fUS data is inherently multidimensional, we propose using...

Towards adjoint-based mesh refinement for Large Eddy Simulation using reduced-order primal solutions: Preliminary 1D Burgers study

Adaptive Mesh Refinement (AMR) is potentially an effective way to automatically generate computational meshes for high-fidelity simulations such as Large Eddy Simulation (LES). When combined with adjoint methods, which are able to localize error contributions, AMR can generate meshes that are optimal for computing a physical quantity of...

A singular values approach in helicopter gas turbine engines flight testing analysis

The process of empirical models evaluation is at the core business of experimental flight-testing data analysis. Accurate and convenient flight-testing of helicopter engine(s) available power is crucial for predicting the total helicopter performance. Common practice in estimation of in-flight helicopter gas turbine engine power consists of a...

An Efficient Method for Multi-Parameter Mapping in Quantitative MRI Using B-Spline Interpolation

Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated Bloch simulations can be avoided by matching the measured signal with a precomputed signal dictionary on a discrete parameter grid (i.e. lookup table...

A Singular-Value-Decomposition-Based Method For Impact Assessment Of Power Electronic Interfaced Generation On The Harmonic Response In Electrical Power Systems

The connection of offshore wind turbines to the European grid has been growing in the recent years. Many European countries are adopting this renewable energy and are increasing the number of wind power plant additions into their electrical transmission networks. Due to the phase out of conventional power plants (e.g. fossil fuel fired), new...

The evolution of electricity price on the German day-ahead market before and after the energy switch

Germany is a forerunner in developing renewable energies. It is therefore of considerable interest to investigate the impact of switch to renewables on the market during transition era. The aim of this study is in two parts: 1) Investigating the volatility; and 2) Conducting a descriptive study on the evolution of daily profiles and emergence...

Short-term scenario-based probabilistic load forecasting: A data-driven approach

Scenario-based probabilistic forecasting models have been explored extensively in the literature in recent years. The performance of such models evidently depends to a large extent on how different input (temperature) scenarios are being generated. This paper proposes a generic framework for probabilistic load forecasting using an ensemble of...

Reduced Order Modelling methodologies for proton therapy applications

In proton therapy, the calculation of the dose distribution is of great importance in order to find the best treatment plan. For a treatment plan with high quality, several error scenarios are investigated, in order to come up with a general plan that suits best in these scenarios. All of the scenarios have a different simulated error, e.g., in...

Large-scale SVD algorithms for Latent Semantic Indexing, Recommender Systems and Image Processing

Over the past three decades, the singular value decomposition has been increasingly used for various big data applications. As it allows for rank reduction of the input data matrix, it is not only able to compress the information contained, but can even reveal underlying patterns in the data through feature identification. This thesis explores...

The cocktail party problem: GSVD-beamformers for speech in reverberant environments

Hearing aids as a form of audio preprocessing is increasingly common in everyday life. The goal of this thesis is to implement a blind approach to the cocktail party problem and challenge some of the regular assumptions made in literature. We approach the problemas wideband FD-BSS. From this field of research, the common assumption of contineous...

Large Scale Solar Power Integration in Distribution Grids: PV Modelling, Voltage Support and Aggregation Studies

Long term supporting schemes for photovoltaic (PV) system installation have led to accommodating large numbers of PV systems within load pockets in distribution grids. High penetrations of PV systems can cause new technical challenges, such as voltage rise due to reverse power flow during light load and high PV generation conditions. Therefore,...

Reconstructie van de aardkorst met tomografie (Reconstruction of the earth’s crust by tomography)

Röntgenfoto’s, echo’s en reconstructies van de aardkorst zijn drie toepassingen waarbij tomografie een grote rol speelt. Tomografie is een methode om eigenschappen van een onbereikbaar gebied te bepalen met behulp van stralen of drukgolven. De aardkorst bestaat uit allerlei verschillende steenlagen. Als op grote diepte een aardbeving plaatsvindt...