Physiological signals when subjected to digital signal processing algorithms often reveal information about their origin and how they are regulated. Recently, it has been shown that when power spectrum of the heart rate variability signal is computed, physiological mechanisms about how the autonomic nervous system modulates the sinus node of the heart can be unraveled. During the past several years, computation of the power spectrum of heart rate variability has progressed from Blackman-Tukey algorithm and autoregressive modelling to Wigner-Ville distribution. In this thesis, we describe the development of appropriate algorithms for QRS detection from an ECG signal to obtain a heart rate signal, interpolation of heart rate variability signal and the computation of power spectrum. We also describe mathematical details underlying time-frequency analysis, specifically for the Wigner-Ville distribution. We present a software package in C++, for computing the Wigner-Ville distribution of the heart rate variability signal. As applications of these methods in physiology and clinical medicine, we found that the power spectrum of the heart rate variability of premature infants can help us understand the ontogeny of the autonomic nervous system. Similarly, physiological effects of atropine, methacholine and allergen challenges can be elucidated using the power spectrum of heart rate variability in small animals, such as a rat model. Furthermore, a progressive tilt model in human subjects is used to compare power spectrum obtained from the Blackman-Tukey method, autoregressive modelling and the Wigner-Ville distribution. Finally, an application of the Wigner-Ville distribution technique to study the changes that take place in the autonomic regulation of the heart during different stages of sleep is presented. / Thesis / Master of Engineering (ME)

Electromyographic (EMG) signals contain both neural and muscle information. Consequently, EMG signals can be modelled as the composition of two component signals, one of these being a low frequency neural input, the other a relatively high frequency, constant spectrally shaped, stationary, unitary muscle response. Utilizing this model and homomorphic processing estimates of the two component signals can be obtained. These estimates contain neural and muscle information respectively. This thesis establishes the basis for the use of this multiplicative model. It also outlines the application of multiplicative homomorphic processing to EMG signals. The results of this processing are shown to be valid and to contain useful information. The thesis concludes that the model is both appropriate and useful. It also points out that the use of this model and homomorphic processing allows the simultaneous extraction of both neural and muscle information from the EMG signal,a result which is not possible with other currently used processing techniques. / Thesis / Master of Engineering (ME)

Measuring construction tool activity has a potential to improve tool productivity, reduce down-time and give insights to various construction processes. Today, a lot of data is being recordedfrom a construction site. This research aims to explore the technical feasibility of handheld powertool activity recognition with real-time tri-axial accelerometer data. The present study has threefocus areas: 1) Data collection using real-time accelerometer data from two tools: a combihammerand a screwdriver. 2) Hand-craft time and frequency domain features from the collected data. 3)Develop two classification algorithms, namely decision trees and random forest, with hand craftedfeatures to detect tool usage activities. The hand-crafted features provide an understanding of themechanical properties of the tools. For the combihammer, the activities recognized were hammerdrilling, chiseling and motor stop. The activity recognition accuracy was 79% with a decision treeand 80.8% with a random forest. For the screwdriver, the activities recognized were screwing,unscrewing and motor stop. The activity recognition accuracy was 87.7% for a decision tree and94.5% for a random forest algorithm. Variance from time domain and energy from frequencydomain were detected as the high importance features by both the classification algorithms forboth the tools.

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Real-time imaging can be used to monitor, analyze and control medical treatments. In this thesis, we want to explain the spatiotemporal motion and thus enable more advanced procedures, especially real-time adaptation in radiation therapy. The motion occurring between image acquisitions can be quantified by image registration, which generates a mapping between the images. The contribution of the thesis consists of three papers, where we have used different approaches to estimate the motion between images. In Paper I, we combine a state-of-the-art method in real-time tracking with a learned sparse-to-dense interpolation scheme. For this, we track an arbitrary number of regions in a sequence of medical images. We estimated a sparse displacement field, based on the tracking positions and used the interpolation network to achieve its dense representation. Paper II was a contribution to a challenge in learnable image registration where we finished at 2nd place. Here we train a deep learning method to estimate the dense displacement field between two images. For this, we used a network architecture inspired by both conventional medical image registration methods and optical flow in computer vision. For Paper III, we estimate the dynamics of spatiotemporal images by training a generative network. We use nonlinear dimensional reduction techniques and assume a linear dynamic in a low-dimensional latent space. In comparison with conventional image registration methods, we provide a method more suitable for real-world scenarios, with the possibility of imputation and extrapolation. Although the problem is challenging and several questions are left unanswered we believe a combination of conventional, learnable, and dynamic modeling of the motion is the way forward.

Cognitive Radio (CR) is an emerging wireless communications technology that addresses the inefficiency of current radio spectrum usage. CR also supports the evolution of existing wireless applications and the development of new civilian and military applications. In military and public safety applications, there is no information available about the signal present in a frequency band and hence there is a need for a CR receiver to identify the modulation format employed in the signal. The automatic modulation classifier (AMC) is an important signal processing component that helps the CR in identifying the modulation format employed in the detected signal. AMC algorithms developed so far can classify only signals from a single user present in a frequency band. In a typical CR scenario, there is a possibility that more than one user is present in a frequency band and hence it is necessary to develop an AMC that can classify signals from multiple users simultaneously. One of the main objectives of this dissertation is to develop robust multiuser AMC's for CR. It will be shown later that multiple antennas are required at the receiver for classifying multiple signals. The use of multiple antennas at the transmitter and receiver is known as a Multi Input Multi Output (MIMO) communication system. By using multiple antennas at the receiver, apart from classifying signals from multiple users, the CR can harness the advantages offered by classical MIMO communication techniques like higher data rate, reliability, and an extended coverage area. While MIMO CR will provide numerous benefits, there are some significant challenges in applying conventional MIMO theory to CR. In this dissertation, open problems in applying classical MIMO techniques to a CR scenario are addressed. A blind equalizer is another important signal processing component that a CR must possess since there are no training or pilot signals available in many applications. In a typical wireless communication environment the transmitted signals are subjected to noise and multipath fading. Multipath fading not only affects the performance of symbol detection by causing inter symbol interference (ISI) but also affects the performance of the AMC. The equalizer is a signal processing component that removes ISI from the received signal, thus improving the symbol detection performance. In a conventional wireless communication system, training or pilot sequences are usually available for designing the equalizer. When a training sequence is available, equalizer parameters are adapted by minimizing the well known cost function called mean square error (MSE). When a training sequence is not available, blind equalization algorithms adapt the parameters of the blind equalizer by minimizing cost functions that exploit the higher order statistics of the received signal. These cost functions are non convex and hence the blind equalizer has the potential to converge to a local minimum. Convergence to a local minimum not only affects symbol detection performance but also affects the performance of the AMC. Robust blind equalizers can be designed if the performance of the AMC is also considered while adapting equalizer parameters. In this dissertation we also develop Single Input Single Output (SISO) and MIMO blind equalizers where the performance of the AMC is also considered while adapting the equalizer parameters. / Ph. D.

Listeners with normal-hearing often overlook their ability to comprehend speech in noisy environments effortlessly. Our brain’s adeptness at identifying and amplifying attended voices while suppressing unwanted background noise, known as the cocktail party problem, has been extensively researched for decades. Yet, many aspects of this complex puzzle remain unsolved and listeners with hearing-impairment still struggle to focus on a specific speaker in noisy environments. While recent intelligent hearing aids have improved noise suppression, the problem of deciding which speaker to enhance remains unsolved, leading to discomfort for many hearing aid users in noisy environments. In this thesis, we explore the complexities of the human brain in challenging auditory environments. Two datasets are investigated where participants were tasked to selectively attend to one of two competing voices, replicating a cocktail-party scenario. The auditory stimuli trigger neurons to generate electrical signals that propagate in all directions. When a substantial number of neurons fire simultaneously, their collective electrical signal becomes detectable by small electrodes placed on the head. This method of measuring brain activity, known as electroencephalography (EEG), holds potential to provide feedback to the hearing aids, enabling adjustments to enhance attended voice(s). EEG data is often noisy, incorporating neural responses with artifacts such as muscle movements, eye blinks and heartbeats. In the first contribution of this thesis, we focus on comparing different manual and automatic artifact-rejection techniques and assessing their impact on auditory attention decoding (AAD). While EEG measurements offer high temporal accuracy, spatial resolution is inferior compared to alternative tools like magnetoencephalography (MEG). This difference poses a considerable challenge for source localization with EEG data. In the second contribution of this thesis, we demonstrate anticipated activity in the auditory cortex using EEG data from a single listener, employing Neuro-Current Response Functions (NCRFs). This method, previously evaluated only with MEG data, holds significant promise in hearing aid development. EEG data may involve both linear and nonlinear components due to the propagation of the electrical signals through brain tissue, skull, and scalp with varying conductivities. In the third contribution, we aim to enhance source localization by introducing a binning-based nonlinear detection and compensation method. The results suggest that compensating for some nonlinear components produces more precise and synchronized source localization compared to original EEG data. In the fourth contribution, we present a novel domain adaptation framework that improves AAD performances for listeners with initially low classification accuracy. This framework focuses on classifying the direction (left or right) of attended speech and shows a significant accuracy improvement when transporting poor data from one listener to the domain of good data from different listeners. Taken together, the contributions of this thesis hold promise for improving the lives of hearing-impaired individuals by closing the loop between the brain and hearing aids.

Ever wonder why two people can sing the same note with the same loudness, but sound completely different? Middle C is middle C no matter who sings it, yet for some reason Lucianno Pavarotti1s middle C sounds richer and more beautiful than Bob Dylan1s middle C, for example. But then again, what is beauty in singing? It is a completely biased and abstract concept. To some, Bob Dylan1s voice may epitomize tonal beauty, while to others his voice may be comparable to fingernails on a chalk board. Anyway, differences in tone quality, or timbre, are due to differences in the spectral characteristics in different voices. The Music Muse is a computer program designed to help singers train their voices by showing them the individual components of their voices that combine to produce timbre. In paintings, many colors are combined to produce different hues and shades of color. The individual colors that make up the hue are difficult to distinguish. Similarly in music, harmonics with varying amplitudes combine to create voice colors, or timbres. These individual harmonics are difficult to distinguish by the ear alone. The Music Muse splits the voice up into its harmonic components by means of a Fourier transform. The transformed data is then plotted on a harmonic spectrum, from which singers can observe the number of harmonics in their tone, and their amplitudes relative to one another. It is these spectral characteristics that are important to voice timbre. The amplitudes of the harmonics in a voiced tone are determined by the resonant frequencies of the vocal tract. These resonances are called formants. When a harmonic that is produced by the vocal cords has a frequency that is at or near a formant frequency, it is amplified. Formants are determined by the length, size, and shape of the vocal tract. These parameters differ from person to person, and change during articulation. Optimal tonal quality during singing is obtained by placing formants at a desired frequency. The Music Muse calculates the formants of the voice by means of cepstral analysis. The formants are then plotted. With this tool, singers can learn how to place their formants. One of the difficulties of voice training is that singing is rated on a scale of quality, which is difficult to quantify. Also, feedback tends to be biased, and therefore subjective in nature. The Music Muse provides singers with the technology to quantify quality to a degree that makes it less of an abstract concept, and therefore more attainable. / Master of Science

This thesis addresses the issue of GPS satellite spoofing using affordable hardware. The approach involves capturing I/Q-samples from GPS-signals and employing a phase interferometry algorithm for direction finding. By determining the direction of a satellite at a given time and comparing this with decoded navigation messages that reveal the satellite's actual location. This method is able to verify the authenticity of the satellite signals where a discrepancy between these locations suggests potential spoofing. Although the project's theoretical contributions are significant, the practical outcomes fell short of the initial ambitions due to various constraints encountered during the study. Nonetheless, the findings provide valuable insights into the detection of GPS spoofing, highlighting both the potential and the limitations of the proposed method within the allotted timeframe.

Active noise control (ANC) is an active noise mitigation method that has in recent years become increasingly prevalent. The method relies on the principle of superposition, canceling unwanted noise through the addition of a second sound wave with the same amplitude but an inverted phase to the first. One of the most common applications of ANC is in hearables, particularly in wireless earbuds. Because of individual differences in ear anatomy, the requirements for an effective ANC system will vary slightly among different users. However, the static nature of most ANC systems in hearables means that they are unable to account for these anatomical differences, resulting in inconsistent noise reduction across individuals. The aim of this project is to develop an adaptive ANC system capable of accounting for individual variations in ear anatomy through the use of optimization algorithms and adaptive filters. The proposed adaptive ANC system is designed to operate as a separate layer alongside the static ANC system and is implemented in a simulated environment with the help of Python. The effectiveness of the adaptive system is evaluated relative to the static system in terms of overall sound pressure level (OASPL) as well as power spectral density (PSD) across several test participants. The results indicate that the adaptive system indeed provides a noticeable improvement over the standalone static system.