Deliberately mutilated weapons and other objects are repeatedly discovered in ancient burials from the Iron Age. This research is focused on the Early Iron Age bent swords from the Hallstatt C period (800-600 BC) found in archaeological sites in the Netherlands. Metallographic research methods are used to investigate how these swords were bent, i.e., using a blacksmith’s fire or brute force. This elaborates on the Early Iron Age culture as it infers what kind of knowledge and skills were required for the bending process. With the help of a blacksmith, we created a replica to analyse the effect of different types of bending on the microstructure. This is compared with museum samples. Using optical microscopy and SEM(-EBSD) the microstructure of the museum sample and the replica are analysed for signs of deformation. Elemental analysis (SEM-EDS) is used on slag inclusion to estimate the initial iron and sword production processes. EPMA analysis was used to determine the carbon concentration throughout the samples, suggesting the use of wrought iron and hardening techniques. Results show that the Heythuysen sword contains multiple microstructure phases with various carbon concentrations. Most probably a combination of piling techniques and carburisation was applied during the production of the Heythuysen sword. The several bending methods of the replica show a distinction in the microstructure on the level of local misorientation. This is sensitive to the presence of inclusions and the changes in phase and grain size, which complicated the evaluation of the Heythuysen sword. The Heythuysen sword does not show strong evidence of bending by brute force and is most likely bent by a blacksmith with a fire.

Macro X-ray fluorescence (MA-XRF) is a recently developed technology allowing to obtain elemental information from cultural heritage objects. This information can, for example, be used to identify pigments used in a painting. Yet, the extended period of time it takes to scan an object is a major issue within MA-XRf. For instance, it took about 60 days to scan the Ghent Altarpiece. The long scanning time is a consequence of the necessary dwell time per pixel to create a robustly interpretable spectrum: the higher the dwell time, the higher the signalto-noise ratio (SNR), hence, the easier to detect elements. This thesis explores a possible solution for this problem using a denoising algorithm that increases the signal-to-noise ratio post-acquisition by exploiting the similarity between neighbouring pixels and spectra. To this end, a customized method of wavelet filter bank denoising is proposed. Current thresholding methods used in wavelet filter bank denoising are not suitable for filtering MA-XRF data, therefore, a novel thresholding method is introduced. Here, the widely used universal thresholding method is used as a basis, for which the formula for calculating the standard deviation of the detail coefficients of a channel is altered. Several design parameters of wavelet filter bank denoising were evaluated using a synthetic dataset, for which the performance quality indicators root mean square error (RMSE), mean absolute error (MAE) and SNR were determined. The parameters for which we optimized were the mother wavelet, the number of decomposition levels, and the number of neighbouring channels used for determining the standard deviation σ for thresholding. Good performance was obtained with the haar, db2, and coif1 wavelets, all at 3 levels of decomposition. A suitable number of neighbouring channels depended on the decomposition level and was determined to be 3 (on each side of the channel). Herewith, the signal-to-noise ratio was improved for both the average pixel spectra and the sum spectrum. The filtered synthetic dataset simulated to have a dwell time of 0.5 seconds had a SNR approximately equal to the raw synthetic dataset simulated to have a dwell time of 0.75 seconds. Hence, the algorithm succeeded in lowering the necessary dwell time. A case study of a daguerreotype was used to test the proposed denoising algorithm.

In cable driven parallel robots (CDPRs), the end effector or moving platform is actuated by multiple cables in parallel that are wound on winches, which are located on a frame. Compared to classical parallel robots, such as the Delta robot, CDPRs have a lower inertia due to low cable masses. Therefore, they can perform high speed motions with a low power consumption. Additionally, the workspace of a CDPR is easily scalable as the cable lengths are hardly limited. Consequently, the CDPRs can potentially improve efficiency and reduce the cost of high speed pick and place operations, which are now often carried out by Delta robots. However, CDPRs have not yet been applied in the high speed pick and place industry. One of the reasons that CDPRs are not yet attractive for this industry is their limited orientation range. In pick and place applications it is often required to not only translate a product, but also reorient it about one axis for proper packaging. This motion is also known as a Schönflies motion. For full product reorientation, a rotation of 180 degrees is required, which can only be achieved with an additional axis on the moving platform. Several solutions for large rotations of CDPRs exist in literature, but none of them are designed, compared, modelled or tested for dynamic purposes. Therefore, this thesis proposes three concept designs of CDPRs that can perform a Schönflies motion, including a rotation of 180 degrees. These concept designs are compared with each other and on a state of the art Delta robot, based on their dynamic workspace. The dynamic workspace volume of each concept is optimized for their geometric parameters by the particle swarm algorithm, which showed that the concept that uses a cable loop to perform the rotation has the largest workspace for the smallest cable forces. Additionally, a prototype of this concept has been evaluated on a typical pick and place motion, which shows the feasibility of this concept. Nonetheless, stiffness should improve to reach the state of the art repeatability in future designs.

Retrieving actionable information from large datasets is increasingly computationally expensive due to the current trend of ever-increasing dataset sizes. Reducing dataset sizes with dimensionality reduction techniques is often necessary for statistical analysis techniques, such as classification, to be computationally feasible. Most dimensionality reduction methods do not require any additional information to accomplish their task. However, datasets used for classification, for example, are accompanied by a set of class-labels as well. This extra information can improve dimensionality reduction techniques by explicitly preserving features that explain differences between classes. A field where high-dimensional and large datasets are standard is Imaging Mass Spectrometry (IMS), a technique that simultaneously records the abundance and spatial location of molecules throughout biological tissue samples. Classification has been applied to IMS datasets for a wide range of scenarios, including the diagnosis of disease, distinguishing between tumour types for personalized treatment, and identifying biomarkers. A recently introduced dimensionality reduction method called Soft Discriminant Map (SDM), designed to incorporate class information and prevent overfitting when used on high-dimensional datasets, is a promising candidate to reduce the size and dimensionality of IMS datasets. However, SDM currently requires manual setting of a free parameter β that influences class separation in the newly constructed feature-space. This thesis explores the use of SDM on IMS datasets in classification use cases and proposes a framework to set β in a data-driven way: Data-Driven Soft Discriminant Map (DD-SDM). Furthermore, the sensitivity of the classification performance to changes in β is examined. DD-SDM is compared to similar state-of-the-art dimensionality reduction methods in terms of classification performance. The performed experiments show that DD-SDM successfully finds a value for β where the classification performance is on par with, or in some scenarios better than, state-of-the-art dimensionality reduction methods while using fewer features. Setting β either too low or too high results in a suboptimal feature space and worsens classification performance. Golden section search, the search strategy used to find the optimal β in DD-SDM, succeeds in finding the optimal β in fewer iterations than more naive methods. With the use of an artificial dataset in combination with a novel evaluation metric, the Peak Conservation Score (PCS), the distinctive ability of DD-SDM to discard features that are common between classes and to actively select for discriminative features is demonstrated. The DD-SDM framework is furthermore applied to real-world IMS measurements of rat brain and mouse kidney tissue.