PhD Thesis Defence


Patrick Fuchs

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MSc SS Thesis Presentation

Deep Learning-Based Sound Identification

Shaoqing Chen

Environmental sound identification and recognition aim to detect sound events within an audio clip. This technology is useful in many real-world applications such as security systems, smart vehicle navigation and surveillance of noise pollution, etc. Research on this topic has received increased attention in recent years. Performance is increasing rapidly as a result of deep learning methods. In this project, our goal is to realize urban sound classification using several neural network models. We select log-Mel spectrogram as the audio representation and use two types of neural networks to perform the classification task. The first is the convolutional neural network (CNN), which is the most straightforward and widely used method for a classification problem. The second type of network is autoencoder based models. This type of model includes the variational autoencoder (VAE), beta-VAE and bounded information rate variational autoencoder (BIR-VAE). The encoders of these systems extract a low dimensionality representation. The classification is then performed on this so-called latent representation. Our experiments assess the performances of different models by evaluation metrics. The results show that CNN is the most promising classifier in our case, autoencoder-based models can successfully reconstruct the log-Mel spectrogram and the latent features learned by encoders are meaningful as classification can be achieved.

PhD Thesis Defence


Aydin Rajabzadeh

Compared to metals, composite materials offer higher stiffness, more resilience to corrosion, have lighter weights, and their mechanical properties can be tailored by their layup configuration. Despite these features, composite materials are susceptible to a diversity of damages, including matrix cracks, delamination, and fibre breakage. If these damages are not detected and mended, they can spread and result in the failure of the whole structure. In particular, when the structure is under fatigue and vibrations during flight, this process can expedite. Moreover, if such damages occur in the internal layers of the composite material, they will be difficult to detect and to characterise. There is thus a huge demand for reliable and accurate structural health monitoring methods to identify these defects. Such methods either try to monitor the structural integrity of the composite during service, or they are used for studying a desired configuration of a composite material during fatigue and tensile tests. This thesis provides structural health monitoring solutions that can potentially be used for both these categories. The structural health monitoring applications developed in this thesis range from accurate strain and displacement measurement, to detection of cracks and the identification of damages in composites.

In this thesis, fibre Bragg grating (FBG) sensors were chosen for this purpose. The miniature size and small diameter of these sensors makes them an ideal candidate for embedding them between composite layers, without severely altering the mechanical properties of the host composite material. They can thus provide us with direct information about the current state of the laminated composite, potentially at any depth. This is especially useful for acquiring information about the internal layers of the composite material, as barely visible impact damages and micro-cracks often form beneath the surface of the material without being visible on its exterior.

In spite of their interesting physical characteristics, applications of FBG sensors are typically limited to point strain or temperature sensors. Further, it is often assumed that the strain field along the sensor length is uniform. For this reason, there is currently a gap in the field of structural health monitoring in retrieving meaningful information about the non-uniform strain field to which the FBG sensor is subjected in damaged structures. The focus of this thesis is on analysing the response of FBG sensors to highly non-uniform strain fields, which are a characteristic of the existence of damage in composites.

To tackle this problem, first a new model for the analysis of FBG responses to nonuniform strain fields will be presented. Using this model, two algorithms are presented to accurately estimate the average of such non-uniform axial strain fields, which conventional strain estimation algorithms fail to deliver. In fact, it is shown that the state-of-the-art strain estimation methods using FBG sensors can lead to errors of up to a few thousand microstrains, and the presented algorithms in this thesis can compensate for such errors. It was also shown that these methods are robust against spectral noise from the interrogation system, which can pave the way for more affordable FBG based strain estimation solutions.

Another contribution of this thesis is the demonstration of two new algorithms for the detection of matrix cracks, and for accurate monitoring of the delamination growth in composites, using conventional FBG sensors. These algorithms are in particular useful for studying the mechanical behaviour of laminated composites in laboratory setups. For instance, the matrix crack detection algorithm is capable of characterising internal transverse cracks along the FBG length during tensile tests. Along the same lines, the delamination growth monitoring algorithm can accurately localise the delamination crack tip along the FBG length in mode-I tensile and fatigue tests. These algorithms can perform in real-time, which makes them ideal for dynamic measurement of crack propagation under fatigue, and their spatial resolution and accuracy is superior to the other state-of-the-art damage detection techniques.

Finally, to enhance the precision of the damage detection schemes presented in this thesis, two different methods are proposed to accurately determine the active gauge length of the FBG sensor, and its position along the optical fibre. This information is generally not provided for commercial FBG sensors with such accuracy, which can adversely affect the precision of crack tip localisation algorithms. Following the algorithms provided in this thesis, the sensor position can be marked on the optical fibre with micrometer accuracy.

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PhD Thesis Defence

MEMS Solutions For More Than Illumination

Xueming Li

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PhD Thesis Defence

From Silicon Toward Silicon Carbide Smart Integrated Sensors

Luke Middelburg

This PhD thesis focusses on the possibilities and challenges of the pathway from silicon toward silicon carbide smart integrated sensors. The research toward extended functionality of sensors in state-of-the-art silicon technology and the exploration of the application of wide-bandgap semiconductors can both be seen as realization of the More-than-Moore trend, described by diversification, the introduction of novel materials and integrated process development.
In this context, different types of sensors are developed, such a high-resolution gravimeter in silicon technology and different poly-SiC-based sensors such as a platform for an optical PM sensor and different pressure sensing structures. Additionally, a SiC CMOS chip is developed in collaboration with Fraunhofer IISB consisting of discrete electronic devices, resistive and capacitive read-out circuits and temperature sensors.

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