Active phased array antennas, generally consisting of a large number of antenna elements and integrated-circuit (IC) components, are essential for different applications like 5G/6G communications, radar, aerospace, satellite communications and so on. The study of radiation pattern measurements and analysis of pattern data is a pivotal step in characterizing and understanding the use of antennas in any application. Unwanted changes can be observed in the radiation pattern due to randomly failed elements, ICs, or IC channels in phased arrays. Hence, there is a vital need for fast testing and diagnosis to identify possible failures of these large antenna systems, before taking any immediate action to compensate for these changes. Due to integrated-circuits (IC) non-linearities and mutual coupling challenges, the existing deterministic and optimization-based fault detection techniques fail to provide reliable results in practical systems. Besides, it is desired to achieve real-time array diagnosis during in-field operation, which makes the problem even more challenging. In this thesis, a novel machine-learning assisted fault-detection algorithm is proposed which is trained by using sparse far-field measurements from a practical IC-integrated millimeter-wave (mm-wave) phased array prototype and fixed multi-probe measurement system (antenna dome). A global optimization approach is used as a benchmark for comparison to the state-of-the-art. The incorporation of machine learning techniques greatly improves the fault detection time and accuracy in comparison to global optimizers. Furthermore, studies based on the number of sampling points used and the varying number of faults are also conducted which indicate similar conclusions.

This thesis investigates whether an EEG headset can be used to distinguish motor imagery signals in real time for a Brain Computer Interface (BCI).The specific EEG headset used for this project is the gtec Unicorn Hybrid Black. The aim of this subgroup is to stream the data in real time, preprocess the data, and create a training dataset using recordings from subjects. The sample from the EEG headset is streamed to a laptop using a live data streaming framework called Labstreaminglayer. The data is then filtered using frequency filters and ICA to remove noise and artifacts. Finally, ERDS plots are used to check the signal quality of the recordings. Recordings of sufficient quality should have (de)synchronisation peaks after the prompt is displayed. Before the recordings are made, an experimental setup is set up. This includes prompts with four different movements that the subject is asked to imagine. The data is sent to a GUI to be visualized with various graphs. It is also sent to a machine learning model to classify the movements. It was concluded that the subsystem could successfully stream data from the cap, process the data, and verify the quality of the data. Using ERDS plots, it was verified that some, but not all, MI actions are distinguishable for one individual. However, further verification of other individuals is required to conclude whether this is a systematic problem or is due to the individual.

Microscopy is a crucial tool across various scientific domains. Due to light diffraction, the images acquired from optical microscopes are usually blurry and corrupted by noise. For an accurate quantitative analysis, the measured images need to be deconvolved to achieve higher resolution. Deconvolution processes are computationally expensive, due to the large data size. This leads to out-of-memory issues and extended computation time.

To address these problems, this project aims to develop a novel convolution scheme. It utilizes the special structure of the Point Spread Function, which in conventional microscopy techniques has most of its energy concentrated in the center. And implement multi-resolution signal processing methods. This approach enhances computational efficiency while retaining computational accuracy.

Reconfigurable Intelligent Surfaces (RIS) are envisioned to become a pivotal transformative technology within the realm of 6G mobile networks. In this study, we introduce three heuristic algorithms designed to optimize radio resource management, ultimately enhancing throughput within a RIS-enhanced mobile network. Our findings demonstrate that the algorithm which holistically optimizes user scheduling, RIS-UE association, cell precoding matrices, and RIS configuration matrices outperforms alternative strategies.

Moreover, our investigation delves into uncovering the most effective applications of RIS. This involves a thorough performance comparison of the algorithms across diverse scenarios, encompassing varying RIS deployment configurations (position and orientation), number of users in a network, and number of RIS elements. Additionally, we model the influence of blocker loss—characterized by blocker presence probability and strength—on throughput performance.

In the wake of our study, it becomes evident that RIS exhibits the most promising potential in scenarios involving MU-MIMO configurations, whether within single-cell or multi-cell layouts, and for both indoor and outdoor user settings. However, for SU-MIMO cases, RIS-induced throughput enhancement manifests exclusively in single-cell layouts, and particularly benefits outdoor users in environments marked by substantial blocker strength.

Ambiguities are an often encountered nuisance in signal processing and are the source of some of the fundamental trade-offs encountered in radar systems. The goal of this thesis is to extract unambiguous information about targets by combining a limited amount of measurements on a video integration level. A novel framework is proposed to reach this goal. At the heart of the framework lives a relevance vector machine which is extended to process the ambiguities on a video integration level and to work off-grid. The relevance vector machine is then extended to become the ambiguity aware relevance vector machine. This extension is either performed by a frequentist test or by estimating a posterior distribution. The frequentist test is used to test whether we can statistically significantly discern the returned output from ambiguities. The posterior is estimated according to Bayes’ theorem and thus allows for the incorporation of prior information. In this thesis, the framework is specifically applied to Doppler processing of a pulse-Doppler radar system. Compared to existing methods for estimating unambiguous Doppler velocity in a multi-target environment, the framework provides a general increase in performance, allows for the incorporation of prior information, and is able to give a measure of confidence in the estimates. A simulation study is set up to show the performance increase. This simulation study also highlights the utility of incorporating prior information and the quantification of uncertainty.

Automatic Dependent Surveillance Broadcast (ADS-B) allows aircraft to broadcast their own position, speed, altitude, and other information to ground stations and other nearby aircraft. This information is then used by air traffic control for situational awareness, and collision avoidance. ADS-B spoofing is possible due to the lack of authentication and encryption in the ADS-B protocol. This can result in incorrect decision-making and potential safety hazards. Validation of the location of the ADS-B message is required for Luchtverkeersleiding Nederland (LVNL) such that it can maintain separation minima between civil aircrafts, whilst using ADS-B operationally.
Analysis of possible approaches for ADS-B validation has resulted a multilateration (MLAT) based approach. Time of Arrival (TOA) measurements of the ADS-B messages are used to validate the location. For validation, at least two Ground Stations (GS) are required instead of the four GSs required for a MLAT track, allowing for ADS-B validation in a larger area than it is currently used for in a tracking application. If an ADS-B message is considered validated, its content can be used by ATC. Therefore MLAT based validation results in an increased surveillance coverage. Validation is achieved in two steps, first a tracker is used to compute the state of the target using the TOA measurements, secondly this state is compared to the ADS-B location using a likelihood ratio test.
Tracking is done using a Sequential Importance Resampling (SIR) Particle Filter (PF). Classical PF issues as the degeneracy problem and sample impoverishment problem are mitigated by using a novel sampling method that samples directly from the measurement at the initialization of the SIR filter. Without this novel method a traditional SIR filter (where the proposal density is uniformly distributed) requires roughly a million particles to converge on the location of the target. Below this amount of particles the traditional SIR filter fails. The proposed SIR filter can converge on the location of the target using only 1000 particles.
To provide LVNL options and insights, three different likelihood ratio tests are proposed, namely the Minimum Bayes Risk, The Neyman-Pearson and the Minimax Hypothesis test.
Performance of the algorithm is investigated using a case study where data from LVNL’s Surveillance Data North Sea (SDNS) MLAT system is used. Results have found that each test is capable of correct ADS-B validation. The limiting factor in the validation algorithm is the quality of the state estimate. At lower altitudes (<FL20) state estimation can fail and therefore also the hypothesis test. Above this altitude, spoofed targets can be detected if the distance between the spoofing transmitter and the location inside the spoofed message is roughly 1000 to 2000 meters depending on the hypothesis test used. Horizontally, this falls within LVNL’s separation minima, vertically, this falls outside the separation minima.

This paper reports the design of a part of a genetic algorithm, which is made to design analog filters for loudspeakers. The part in this report is the part which deals with the representation of electrical filter circuits, the mutation of these filters, and finding their transfer function. The considered representations are graph coding and a tree data structure. They are compared on intuitiveness, how well mutations can be performed, and the complexity of calculating the transfer function. The tree structure is reasoned to be the most suitable. Described is which mutations can be performed on a filter by the final program, as well as how embryo circuits are made, how the transfer function is calculated, and which hyperparameters were designed and how they were set. Finally, the design is implemented using Python and the operations are tested.

This document presents the development of a user interface for an EEG motor imagery based Brain-Computer Interface (BCI) as the interface subgroup. The aim of this subgroup in the project was to design and implement a graphical user interface (GUI) incorporating visual neurofeedback to enhance the accuracy of the decode algorithm, developed by the other subgroup, during the calibration/training stage for the user such that the user will have better motor imagery control using the GUI. The GUI features two interactive games, namely pong and breakout, and incorporates topographic maps displaying the user’s brain activity. The primary goal of these maps was to improve the training process of the algorithm for each individual user. Additionally, the thesis explores the feasibility of incorporating steady-state visually evoked potential (SSVEP) elements in the games or for creating a new game that combines motor imagery and SSVEP elements allowing for more complex games to be played thus positively affecting the user experience.

This project develops a channel estimation technique for millimeter wave (mmWave) communication systems. Our method exploits the sparse structure in mmWave channels for low training overhead and accounts for the phase errors in the channel measurements due to phase noise at the oscillator. Specifically, in IEEE 802.11ad/ay-based mmWave systems, the phase errors within a beam refinement protocol packet are almost the same, while the errors across different packets are substantially different.

We show that standard compressed sensing algorithms that treat phase noise as a constant fail when channel measurements are acquired over multiple beam refinement protocol packets. Most of the methods that have addressed this problem treat phase noise as purely random, missing the inherent structure within the measurement packets. We present a novel algorithm called partially coherent matching pursuit for sparse channel estimation under practical phase noise perturbations. The proposed approach leverages this partially coherent structure in the phase errors across multiple packets. Our algorithm iteratively detects the support of sparse signal and employs alternating minimization to jointly estimate the signal and the phase errors.

We numerically show that our algorithm can reconstruct the channel accurately at a lower complexity than the benchmarks, and derive a preliminary support detection bound as a performance guarantee.

This study delves into the application of coded covers in enhancing Acoustic Vector Sensor (AVS) performance for sound source localization. We initially explored the use of a coded mask inspired by ultrasound imaging. However, our analysis indicated that the coded mask primarily acts as a scaling factor without substantial benefits for direction of arrival (DOA) estimation.
In response, we investigated an alternative approach using a larger cover with spaced channels, positioned above the AVS sensor. In this setup, each channel operates independently, and their combined acoustic outputs are analyzed upon reaching the sensor. Importantly, the upper surface channels can be viewed as a virtual uniform linear array (ULA), allowing the application of compressed analysis methods.
Two distinct DOA estimation approaches were developed: the compressive sampling (CS) method and the compressive covariance sensing (CCS) method. Both methods were validated, showing improved accuracy in angle estimation. Notably, the CCS method exhibited the potential to expand the number of detectable sound sources using a single AVS.
In summary, while the coded mask did not offer significant advantages, the coded cover design, along with the CS and CCS methods, improved DOA accuracy and extended the detection capabilities of single AVS.

Tabular data synthesis is a promising approach to circumvent strict regulations on data privacy. Although the state-of-the-art tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, they are sensitive to column permutations of input data. In this work, we conduct an impact and root-cause analysis of sensitivity to column permutations through extensive empirical analysis. Specifically, we show that changing the input column order increases the statistical difference between real and synthetic data by up to 39\%, due to the encoding of tabular data. To address this challenge, we first attempts to find an optimal column order to improve tabular data synthesis. Next, we propose AE-GAN, an effective tabular data synthesizer that leverages the latent representation of tabular data to regulate its sensitivity to column permutations while incurring low training overhead. AE-GAN is composed of an Autoencoder (AE) to efficiently represent tabular data as latent vectors, a Generative Adversarial Network (GAN) to generate realistic synthetic data, and a classifier to improve the semantic integrity of the generated records. It combines the flexibility of unsupervised training with the control offered by supervised training, thereby ensuring the statistical similarity between real and synthetic data. The evaluation of AE-GAN on five datasets shows that it is not only more permutation-invariant than the prior state-of-the-art, but also results in better downstream analysis based on the generated data.

Automatic Speech Recognition (ASR) systems have seen substantial improvements in the past decade; however, not for all speaker groups. Recent research shows that bias exists against different types of speech, including non-native accents, in state-of-the-art (SOTA) ASR systems. To attain inclusive speech recognition, i.e., ASR for everyone irrespective of how one speaks or the accent one has, bias mitigation is essential and necessary. In this thesis, two SOTA ASR systems (one is based on the recurrent neural network (RNN) and the other is based on the transformer architecture) are built to uncover and quantify the bias against non-native accents. Here I focus on bias mitigation against non-native accents using two different approaches: data augmentation and by using more effective training methods. For data augmentation, an autoencoder-based cross-lingual voice conversion (VC) model is used to increase the amount of non-native accented speech training data in addition to data augmentation through speed perturbation. Moreover, I investigate two training methods, i.e., fine-tuning and Domain Adversarial Training (DAT), to see whether they can utilize the available non-native accented speech data more effectively than a standard training approach. Experimental results show for the transformer-based ASR model: (1) adding VC-generated and speed-perturbed data to train the ASR model gives the best bias mitigation performance and the lowest word error rate (WER); (2) fine-tuning reduces the bias against non-native accents but at the cost of native accent performance; and (3) compared with the standard training method, DAT does not leads to further bias reduction. While for the RNN-based ASR model, all the 4 bias mitigation approaches do not show obvious benefits.