In the context of prosthetic devices, the absence of natural sensory feedback poses a significant challenge, leading to limited proprioceptive sensation and reduced control over artificial limbs. This study explores the potential of establishing a magnetic connection between prosthetic devices and muscles to enhance proprioceptive feedback and facilitate more intuitive control of prosthetic limbs. The primary objective is to ascertain the safe range of forces that can be transmitted magnetically from a prosthesis to a muscle, with ethical considerations guiding the allocation of muscle resources. Preliminary experiments conducted using Silicone Ecoflex 0030, chosen for its stress behaviour similarity to natural muscle tissue, serve as the cornerstone for this research. A novel methodology is introduced to predict the forces generated when magnets snap, particularly when one magnet is embedded within an elastic medium like silicone, replicating the force distribution characteristics of muscle tissue. The theoretical model guiding these predictions is based on empirical data collected from experiments that investigate the magnetic force interactions between magnets and the distinct properties of silicone materials. Validation of the theoretical model for predicting the snapping point is achieved through a comprehensive series of final experiments. This study demonstrates that the monopole model, tailored for magnets with parallel-aligned poles, yields highly accurate predictions when compared to empirically measured data concerning magnet force modelling. Additionally, within silicone, the force-displacement relationship exhibits linearity, with stiffness primarily contingent on the length of the implanted object. Force transmission experiments involving a magnet embedded in silicone reveal that magnets snap at forces of less than 3 Newtons. A significant outcome of this research is the development of a validated methodology for predicting the force when a pair of magnet become unstable and snap. This methodology is applicable to scenarios involving magnets embedded in elastic media like silicone and holds the potential for extending predictions to snap forces in muscle tissue. The implications of this study are promising, suggesting the feasibility of employing magnets as sensors to detect muscle intent and as activators to provide feedback by mobilizing implanted magnets, thereby stimulating muscle spindles. These findings open new avenues for enhancing proprioception and control in prosthetic devices.

As 5G is rapidly growing, wireless communication systems require wideband, compact and highly efficient power amplifier modules (PA) to drive the base station antenna arrays. Doherty PAs are implemented in most base stations. The final stage in these power amplifier modules has high supply voltages (28-50 V) to generate a high output power of >20 W at reasonable output impedance levels. This work replaces the ‘classical’ single-ended cascode PA to drive the Doherty PA, with a series push-pull (or totem-pole) PA to increase the efficiency and bandwidth of the driver. The series push pull designs can reach peak efficiencies of 70.7%, which is about 10 percentage points higher than the efficiency of the single-ended PA. To ensure that a fair comparison is made between the designs, the series push-pull designs operate at 5 V and 10 V supply, in order to generate an output voltage swing of 5 V and 10 V respectively. This is compared to the single-ended PA with a 5 V supply and 10 V output swing. Simulation results show that the series push-pull design does indeed increase the efficiency of the driver whilst having minimal AM-AM distortion (<1 dB) at the design frequency of 3.6 GHz.

The goal of this project was to develop a sensor system that detects Rapid Eye Movement Sleep Behaviour Disorder (RBD) episodes in an early stage and brings the patient to a lighter sleep stage at the episode onset. This project was completed in close collaboration with Momo Medical, a fast-growing start-up and developer of the BedSense - a device placed under mattresses that tracks the bed posture and restlessness of residents in nursing homes. To detect RBD episodes, the BedSense was used in combination with a sock that houses the biosensors EDA, PPG and an accelerometer. The sock also contains a vibrator module, that can bring the patient to a lighter sleep stage in case of an episode. The outcomes of this project are promising, data has been recorded from both non-RBD test subjects and an RBD patient, and the vibration module has been tested. The results of this project were integrated with another project seeking to develop an algorithm to detect RBD episodes in an early stage. This project used data from the Momo BedSense and from the sock that has been created in this project.

The objective of this project was to develop a real-time restlessness detection algorithm for Rapid Eye Movement Sleep Behavior Disorder (RBD). This project was completed in collaboration with Momo Medical, a fast-growing start-up, and developer of the BedSense device. Our project aimed to design a system capable of detecting RBD episodes and bringing patients to a lighter sleep stage. To accomplish this, proprietary data and existing data from an RBD patient were collected through the BedSense device. Additionally, data was extracted using a hardware system (developed by another subgroup) from a non-RBD test subject. The collected data was utilized to design and implement a restlessness detection algorithm. Given the limited data collected for this project, a classical detection algorithm was developed instead of machine learning techniques. The software system demonstrated impressive performance, achieving a balanced accuracy of 95.94%, an average sensitivity of 100%, and an average specificity of 91.88%. The results of this project were integrated with another project seeking to develop a sensor system that brings an RBD patient to a lighter sleep stage. This project built a sock system with embedded sensors and a vibration module to achieve this. The proof of concept of the final integrated system demonstrated a non-invasive method of detecting and preventing episodes caused by RBD.

Purpose The main purpose of this report is to find out whether the OpenBCI "Ultracortex Mark IV" Electroencephalogram (EEG) headset is capable of differentiating EEG-signals of motor execution from neutral state with recorded data and to find out whether it can differ motor executions between left and right hand. Next to that, it is to be determined whether the OpenBCI headset was the optimal one for this purpose. Method First, the specifications of different headsets were compared. Afterwards, a montage of the electrodes was designed to detect motor execution and motor imagery, mainly centered around the locations C3, Cz and C4, on the top of the scalp. The software "Openvibe" was used to extract data from the headset during experiments and to record it in a csv file. A subject was asked to follow a video with a sound cue followed by a visual cue instructing to move either its left hand or right hand. Result Merging the left and right hand trial data together, the result is that the headset shows in the alpha band (7-12 Hz) mostly a decrease (ERD) in magnitude around the visual cue, sometimes followed by a bigger increase in magnitude (ERS). Looking at the extremes after the cue, it is seen that mostly the difference in magnitude is around a factor 1.5 compared to the average magnitude of before the visual cue. Splitting the trial data between left and right hand, similar results can be seen, but one hand produces slightly more ERD or ERS than the other hand depending on the position of the electrode on the left or right hemisphere of the brain. Conclusion The OpenBCI headset can in fact detect a difference between movement of the hands and the neutral state. Differentiating between the movements of left and right hands seems possible from the results, but the difference in the signal of left and right hand is minimal. It is recommended to repeat the experiment with more trials and different subjects to get a more solid conclusion.

The thesis explores an innovative technique for enhancing the precision of short-term weather forecasts, particularly in predicting extreme weather phenomena, which present a notable challenge for existing models such as PySTEPS due to their volatile behavior. Leveraging precipitation and meteorological data sourced from the Royal Netherlands Meteorological Institute (KNMI), the research innovates through the development of a physics-informed neural network. Central to this approach is the implementation of a Physics-Informed Discriminator GAN (PID-GAN), a method that embeds physical principles directly into the adversarial training regime. The architecture is marked by the integration of a Vector Quantization Generative Adversarial Network (VQ-GAN) and a Transformer as the generator, complemented by a temporal discriminator as the discriminator component. Results from this study indicate a notable advancement over traditional numerical weather prediction and cutting-edge deep learning models, underscoring the PID-GAN model's superiority in delivering accurate precipitation nowcasting metrics.

Intense precipitation can have extensive economic outcomes, from disrupting outdoor activities to causing severe infrastructural damage, such as landslides, and endangering public safety. The urgency to mitigate these impacts underscores the need for improved early warning systems. Enhanced short-term weather prediction, or nowcasting, is critical for addressing these severe weather events effectively. Traditional meteorological forecasting methods, while foundational, are often constrained by simplistic physical assumptions and fail to capture the complex, nonlinear patterns of intense weather events. These methods also struggle with high computational demands and lack the resolution needed to detect crucial microscale atmospheric phenomena for accurate short-term forecasts. To address these challenges, this research introduces a novel deep learning approach utilizing a streamlined architecture that combines a Vector Quantized Variational Autoencoder (VQVAE) and an Autoregressive (AR) Transformer. This model aims to predict weather conditions up to 180 minutes ahead, using data analyzed at 30-minute intervals. The proposed model displays comparable performance with the state-of-theart conventional methods and other deep learning nowcasting models in predicting precipitations and sometimes extreme events. This study seeks to enhance forecasting accuracy and efficiency, providing valuable contributions to the field of meteorological nowcasting.

Implant-associated infections are a severe concern affecting 1−3 % of patients undergoing primary joint arthroplasty [1]. Magnetic hyperthermia, which can be non-invasively induced by applying an alternating magnetic field (AMF) around a metallic implant, is a new approach with great potential. This research project explores the application of non-contact induction heating (IH) of metallic implants in orthopedic surgery. It focuses on the heating behavior of paramagnetic biomaterials relevant to this application in alternating magnetic fields. Empirical testing plays an inherent role in research. However, it is often very time-consuming, especially when assessing many specimens. With in silico simulations, data can be yielded much faster. An analytical model simulating the heating dynamics of paramagnetic materials in the AMF was constructed to provide a tool for faster suitability assessment of metallic biomaterials for magnetic hyperthermia. The model was then compared with empirical data obtained in a controlled environment. The empirical data showed a quadratically increasing temperature rise with the field amplitude and a linearly growing temperature rise with the frequency. The constructed analytical model confirmed that the heat generated in the materials increases quadratically with the increasing AMF amplitude. Additionally, increasing the frequency of the AMF also affects heat generation. The model predicted different trends depending on what domain a material is assigned to. However, empirical data indicates a consistent linear relationship across several assessed frequencies and materials. Based on the obtained knowledge of the material's behavior in AMF, a new hip implant intended for magnetic hyperthermia treatment was designed. It utilized a coating on the neck of the femoral stem, designed to enable targeted IH, particularly to regions most susceptible to bacterial infections. The experiment featured a multi-material specimen consisting of Ti Gr. 23 and ST. 37, which mimicked the neck of a hip implant, the proof of concept was successfully demonstrated, showing a significant temperature increase for specimens with coating compared to the ones without at high magnetic field strength and frequency. However, targeted IH was not detected. Better computational models can further explore the obtained knowledge, and the proof-of-concept can be further tested to investigate its contribution to treating resistant bacterial biofilms.

Implant-associated infections are a severe concern affecting 1−3 % of patients undergoing primary joint arthroplasty [1]. Magnetic hyperthermia, which can be non-invasively induced by applying an alternating magnetic field (AMF) around a metallic implant, is a new approach with great potential. This research project explores the application of non-contact induction heating (IH) of metallic implants in orthopedic surgery. It focuses on the heating behavior of paramagnetic biomaterials relevant to this application in alternating magnetic fields. Empirical testing plays an inherent role in research. However, it is often very time-consuming, especially when assessing many specimens. With in silico simulations, data can be yielded much faster. An analytical model simulating the heating dynamics of paramagnetic materials in the AMF was constructed to provide a tool for faster suitability assessment of metallic biomaterials for magnetic hyperthermia. The model was then compared with empirical data obtained in a controlled environment. The empirical data showed a quadratically increasing temperature rise with the field amplitude and a linearly growing temperature rise with the frequency. The constructed analytical model confirmed that the heat generated in the materials increases quadratically with the increasing AMF amplitude. Additionally, increasing the frequency of the AMF also affects heat generation. The model predicted different trends depending on what domain a material is assigned to. However, empirical data indicates a consistent linear relationship across several assessed frequencies and materials. Based on the obtained knowledge of the material's behavior in AMF, a new hip implant intended for magnetic hyperthermia treatment was designed. It utilized a coating on the neck of the femoral stem, designed to enable targeted IH, particularly to regions most susceptible to bacterial infections. The experiment featured a multi-material specimen consisting of Ti Gr. 23 and ST. 37, which mimicked the neck of a hip implant, the proof of concept was successfully demonstrated, showing a significant temperature increase for specimens with coating compared to the ones without at high magnetic field strength and frequency. However, targeted IH was not detected. Better computational models can further explore the obtained knowledge, and the proof-of-concept can be further tested to investigate its contribution to treating resistant bacterial biofilms.