Latest Ph.D. Theseshttps://theses.eurasip.org/feeds/atom/theses/2024-03-12T07:56:08+02:00Most recent submissions to EURASIP's Library of Ph.D. Theses.Copyright (c) 2024 EURASIPDirect Pore-based Identification For Fingerprint Matching Process2024-03-12T07:56:08+02:00Vedat DELICAN, PhDhttps://theses.eurasip.org/theses/975/direct-pore-based-identification-for-fingerprint/Fingerprint, is considered one of the most crucial scientific tools in solving criminal cases. This biometric feature is composed of unique and distinctive patterns found on the fingertips of each individual. With advancing technology and progress in forensic sciences, fingerprint analysis plays a vital role in forensic investigations and the analysis of evidence at crime scenes. The fingerprint patterns of each individual start to develop in early stagesof life and never change thereafter. This fact makes fingerprints an exceptional means of identification. In criminal cases, fingerprint analysis is used to decipher traces, evidence, and clues at crime scenes. These analyses not only provide insights into how a crime was committed but also assist in identifying the culprits or individuals involved. Computer-based fingerprint identification systems yield faster and more accurate results compared to traditional methods, making fingerprint comparisons in large databases ...Signal Processing and Learning over Topological Spaces2024-03-11T17:06:51+02:00Battiloro Claudiohttps://theses.eurasip.org/theses/974/signal-processing-and-learning-over-topological/The aim of this thesis is to introduce a variety of signal processing methodologies specifically designed to model, interpret, and learn from data structured within topological spaces. These spaces are loosely characterized as a collection of points together with a neighborhood notion among points. The methodologies and tools discussed herein hold particular relevance and utility when applied to signals defined over combinatorial topological spaces, such as cell complexes, or within metric spaces that exhibit non-trivial properties, such as Riemann manifolds with non-flat metrics. One of the primary motivations behind this research is to address and surmount the constraints encountered with traditional graph-based representations when they are employed to depict intricate systems. This thesis emphasizes the necessity to account for sophisticated, multiway, and geometry-sensitive interactions that are not adequately captured by conventional graph models. The contributions of this work include but ...Non-linear Spatial Filtering for Multi-channel Speech Enhancement2024-02-19T12:10:42+02:00Tesch, Kristinahttps://theses.eurasip.org/theses/973/non-linear-spatial-filtering-for-multi-channel/A large part of human speech communication takes place in noisy environments and is supported by technical devices. For example, a hearing-impaired person might use a hearing aid to take part in a conversation in a busy restaurant. These devices, but also telecommunication in noisy environments or voiced-controlled assistants, make use of speech enhancement and separation algorithms that improve the quality and intelligibility of speech by separating speakers and suppressing background noise as well as other unwanted effects such as reverberation. If the devices are equipped with more than one microphone, which is very common nowadays, then multi-channel speech enhancement approaches can leverage spatial information in addition to single-channel tempo-spectral information to perform the task. Traditionally, linear spatial filters, so-called beamformers, have been employed to suppress the signal components from other than the target direction and thereby enhance the desired ...Contributions to Human Motion Modeling and Recognition using Non-intrusive Wearable Sensors2024-02-16T18:21:27+02:00Gil-Martín, Manuelhttps://theses.eurasip.org/theses/972/contributions-to-human-motion-modeling-and/This thesis contributes to motion characterization through inertial and physiological signals captured by wearable devices and analyzed using signal processing and deep learning techniques. This research leverages the possibilities of motion analysis for three main applications: to know what physical activity a person is performing (Human Activity Recognition), to identify who is performing that motion (user identification) or know how the movement is being performed (motor anomaly detection). Most previous research has addressed human motion modeling using invasive sensors in contact with the user or intrusive sensors that modify the user’s behavior while performing an action (cameras or microphones). In this sense, wearable devices such as smartphones and smartwatches can collect motion signals from users during their daily lives in a less invasive or intrusive way. Recently, there has been an exponential increase in research focused on inertial-signal processing to ...Deep Learning-based Speaker Verification In Real Conditions2024-02-14T14:05:25+02:00Dowerah Sandipanahttps://theses.eurasip.org/theses/971/deep-learning-based-speaker-verification-in-real/Smart applications like speaker verification have become essential in verifying the user's identity for availing of personal assistants or online banking services based on the user's voice characteristics. However, far-field or distant speaker verification is constantly affected by surrounding noises which can severely distort the speech signal. Moreover, speech signals propagating in long-range get reflected by various objects in the surrounding area, which creates reverberation and further degrades the signal quality. This PhD thesis explores deep learning-based multichannel speech enhancement techniques to improve the performance of speaker verification systems in real conditions. Multichannel speech enhancement aims to enhance distorted speech using multiple microphones. It has become crucial to many smart devices, which are flexible and convenient for speech applications. Three novel approaches are proposed to improve the robustness of speaker verification systems in noisy and reverberated conditions. Firstly, we integrate ...Sound Event Detection by Exploring Audio Sequence Modelling2024-02-14T12:18:32+02:00[Pankajakshan], [Arjun]https://theses.eurasip.org/theses/970/sound-event-detection-by-exploring-audio-sequence/Everyday sounds in real-world environments are a powerful source of information by which humans can interact with their environments. Humans can infer what is happening around them by listening to everyday sounds. At the same time, it is a challenging task for a computer algorithm in a smart device to automatically recognise, understand, and interpret everyday sounds. Sound event detection (SED) is the process of transcribing an audio recording into sound event tags with onset and offset time values. This involves classification and segmentation of sound events in the given audio recording. SED has numerous applications in everyday life which include security and surveillance, automation, healthcare monitoring, multimedia information retrieval, and assisted living technologies. SED is to everyday sounds what automatic speech recognition (ASR) is to speech and automatic music transcription (AMT) is to music. The fundamental questions in designing ...Compressive Sensing of Cyclostationary Propeller Noise2024-01-30T10:39:16+02:00Fırat, Umuthttps://theses.eurasip.org/theses/967/compressive-sensing-of-cyclostationary-propeller/This dissertation is the combination of three manuscripts –either published in or submitted to journals– on compressive sensing of propeller noise for detection, identification and localization of water crafts. Propeller noise, as a result of rotating blades, is broadband and radiates through water dominating underwater acoustic noise spectrum especially when cavitation develops. Propeller cavitation yields cyclostationary noise which can be modeled by amplitude modulation, i.e., the envelope-carrier product. The envelope consists of the so-called propeller tonals representing propeller characteristics which is used to identify water crafts whereas the carrier is a stationary broadband process. Sampling for propeller noise processing yields large data sizes due to Nyquist rate and multiple sensor deployment. A compressive sensing scheme is proposed for efficient sampling of second-order cyclostationary propeller noise since the spectral correlation function of the amplitude modulation model is sparse as shown in ...Tensor Decompositions and Algorithms for Efficient Multidimensional Signal Processing2024-01-26T15:27:41+02:00Khamidullina, Lianahttps://theses.eurasip.org/theses/966/tensor-decompositions-and-algorithms-for/Due to the extensive growth of big data applications, the widespread use of multisensor technologies, and the need for efficient data representations, multidimensional techniques are a primary tool for many signal processing applications. Multidimensional arrays or tensors allow a natural representation of high-dimensional data. Therefore, they are particularly suited for tasks involving multi-modal data sources such as biomedical sensor readings or multiple-input and multiple-output (MIMO) antenna arrays. While tensor-based techniques were still in their infancy several decades ago, nowadays, they have already proven their effectiveness in various applications. There are many different tensor decompositions in the literature, and each finds use in diverse signal processing fields. In this thesis, we focus on two tensor factorization models: the rank-(Lr,Lr,1) Block-Term Decomposition (BTD) and the Multilinear Generalized Singular Value Decomposition (ML-GSVD) that we propose in this thesis. The ML-GSVD is an extension ...Robust Network Topology Inference and Processing of Graph Signals2024-01-19T16:28:10+02:00Rey, Samuelhttps://theses.eurasip.org/theses/965/robust-network-topology-inference-and-processing/The abundance of large and heterogeneous systems is rendering contemporary data more pervasive, intricate, and with a non-regular structure. With classical techniques facing troubles to deal with the irregular (non-Euclidean) domain where the signals are defined, a popular approach at the heart of graph signal processing (GSP) is to: (i) represent the underlying support via a graph and (ii) exploit the topology of this graph to process the signals at hand. In addition to the irregular structure of the signals, another critical limitation is that the observed data is prone to the presence of perturbations, which, in the context of GSP, may affect not only the observed signals but also the topology of the supporting graph. Ignoring the presence of perturbations, along with the couplings between the errors in the signal and the errors in their support, can drastically hinder ...Multifunction Radios and Interference Suppression for Enhanced Reliability and Security of Wireless Systems2023-12-18T09:14:21+02:00Pärlin, Karelhttps://theses.eurasip.org/theses/964/multifunction-radios-and-interference-suppression/Wireless connectivity, with its relative ease of over-the-air information sharing, is a key technological enabler that facilitates many of the essential applications, such as satellite navigation, cellular communication, and media broadcasting, that are nowadays taken for granted. However, that relative ease of over-the-air communications has significant drawbacks too. On one hand, the broadcast nature of wireless communications means that one receiver can receive the superposition of multiple transmitted signals. But on the other hand, it means that multiple receivers can receive the same transmitted signal. The former leads to congestion and concerns about reliability because of the limited nature of the electromagnetic spectrum and the vulnerability to interference. The latter means that wirelessly transmitted information is inherently insecure. This thesis aims to provide insights and means for improving physical layer reliability and security of wireless communications by, in a sense, ...