Agenda

Signal Processing Seminar

Elvin Isufi

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Signal Processing Seminar

Carolina Varon

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CAS MSc Midterm Presentations

Stefanie Brackenhoff


PhD Thesis Defence

MRI

Patrick Fuchs

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CAS MSc Midterm Presentations

Hanyu Ma, Preetha Vijayan


CAS MSc Midterm Presentations

Tanmay Manjunath, Roy Arriëns


Signal Processing Seminar

Focussing Waves in Unknown Media

Jörn Zimmerling

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CAS MSc Midterm Presentations

Daniel Kappelle, Randy Prozee


Signal Processing Seminar

Signal Processing for communication

Didem Doğan Başkaya


CAS MSc Midterm Presentations

Bas Liesker


CAS MSc Midterm Presentations

Shreya Sanjeev Kshirasagar


CAS MSc Midterm Presentations

A Generic Framework and Algorithmic Solution for Radar Resource Management

Max Schöpe

Abstract: Recent advances in multi-function radar (MFR) systems led to an increase of their degrees of freedom. As a result, modern MFR systems are capable to adjust many parameters during run-time. An automatic adaptation of the radar system to changing situations, like weather conditions, interference or target maneuvers is usually called radar resource management (RRM). After an introduction to the topic of RRM, I will discuss our approach. We model the different sensor tasks as partially observable Markov decision processes (POMDP) and solve them by applying a combination of Lagrangian relaxation and policy rollout. The algorithm has a generic architecture and can be applied to different radar or sensor systems and cost functions. I will show this through simulations of two-dimensional tracking scenarios. Moreover, I will demonstrate how the algorithm allocates the sensor time budgets dynamically to a changing environment in a non-myopic fashion.

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PhD Thesis Defence

ACCURATE STRUCTURAL HEALTH MONITORING IN COMPOSITES WITH FIBRE BRAGG GRATING SENSORS

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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Signal Processing Seminar

Signal processing in distributed networks; audio signal processing

Metin Çalış

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Signal Processing Seminar

Biomedical signal processing

Aybüke Erol

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MSc SS Thesis Presentation

Distributed Coordination for Multi-feet Truck Platooning

Yikai Zeng

Truck platooning refers to coordinating a group of heavy-duty vehicles at a close inter-vehicle distance to reduce overall fuel consumption. This coordination between trucks is traditionally achieved by adjusting the schedule, velocity and routines to increase the platooning chances, and thus improve the overall fuel efficiency. However, the data model built for the coordination problem is typically integer-constrained, making it generally hard to solve. On the other hand, the interaction among self-interested fleets which are operated by different companies is not well-studied. This thesis aims to build a distributed framework for multi-fleet truck platooning coordination to enable the coordination without a third-party service provider.

The interaction among fleets is considered a non-cooperative finite game, for which we propose the best response search method, which essentially requires to solve a cooperative truck platooning optimization problem iteratively. We refer to the optimization problem as a best-response problem, which is formulated as a mixed-integer linear problem with relaxation skills.

 To achieve a feasible time complexity for the best-response subproblem, we propose a decentralized algorithm, distributing the computational load to connected automated vehicles within the fleet.

The proposed method is examined under a real-world featured demand set to compare the performance in optimality and time complexity with previous studies. The result suggests that the decentralized algorithm delivers the optimal objective value in this case, while the best-response search does not deliver extra benefits as a the dominating time costs in the cost functions eliminate the potential for improvement.


MSc SS Thesis Presentation

Atrial Fibrillation classification from a short single lead ECG recordin

Yuchen Yin

This thesis focuses on classifying AF and Normal rhythm ECG recordings. AF is a common arrhythmia occurring in millions of people every year, which could lead to blood clots, stroke or even heart failure. When AF is occurring, the P waves are often absent and RR intervals are often irregular.

This thesis proposes a new Poincaré plot based feature that exploits the distribution and position information of the plot. The Poincaré plot can visually analyze the nonlinear aspects of the heart rate dynamics both qualitatively and quantitatively. In this thesis, the Poincaré plot values are first quantized into small bins, which represent whether corresponding states are visited by the system or not, by setting ones or zeros. The bins are then given weights by the masks based on the probability of each state being visited by the system, and the relative position between the bins and the center of the plot. By calculating the element-wise multiplication and summation between the quantized Poincaré plot and the masks, the expected value of the matrix of the quantized Poincaré plot is computed, and the outliers in the plot are emphasized. Therefore, the proposed feature is assumed to have a higher value for the AF rhythms and a lower value for the Normal rhythm.

  Instead of RR intervals, the Poincaré plot used in this thesis is also generated from the peak intervals in the autocorrelation function of both ECG and prediction error. The autocorrelation function aims to evaluate the self-similarity of the ECG signals and thus extracts the irregularity of the AF signals.  

The dataset used in this thesis comes from the Physionet Challenge 2017, containing 5076 Normal recordings and 758 AF recordings. In total, 21 Poincaré plot based features are used to train the SVM and random forest models, which yields the F1 score of 0.80 and 0.85, respectively. When using features from the same intervals, RR intervals generate the highest F1 score of 0.77 and 0.81, followed by the peak intervals in the autocorrelation of prediction error with the F1 score of 0.74 and 0.78, followed by the peak intervals in the autocorrelation error of ECG with the F1 score of 0.63 and 0.68. Using the minimum redundancy maximum relevance algorithm, eleven features are selected based on their importance. Training the SVM and RF models with these features reaches the F1 score of 0.78 and 0.84, respectively.

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Signal Processing Seminar

Biomedial Signal Processing

Hanie Moghaddasi

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Signal Processing Seminar

Second Master presentation

Maria Macarulla Rodriguez, Aitor García Manso