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Relevant bibliographies by topics / Biomedical signals processing / Dissertations / Theses

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Dissertations / Theses on the topic 'Biomedical signals processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Ghaderi, Foad. "Signal processing techniques for extracting signals with periodic structure : applications to biomedical signals." Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/55183/.

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In this dissertation some advanced methods for extracting sources from single and multichannel data are developed and utilized in biomedical applications. It is assumed that the sources of interest have periodic structure and therefore, the periodicity is exploited in various forms. The proposed methods can even be used for the cases where the signals have hidden periodicities, i.e., the periodic behaviour is not detectable from their time representation or even Fourier transform of the signal. For the case of single channel recordings a method based on singular spectrum anal ysis (SSA) of the

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2

Samonas, Michael C. "Pre-processing of magnetoencephalographic signals." Thesis, University of Surrey, 1996. http://epubs.surrey.ac.uk/843348/.

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This thesis is concerned with the processing of MagnetoEncephaloGraphic (MEG) signals before their further analysis for clinical purposes. An overview of recent methods that have been applied to brain signals is first presented. The area of interference elimination is covered then, as the MEG signals suffer from the heart interfering magnetic field which in the majority of the experimental situations outweighs the signal of interest. The framework of a two step algorithm which first identifies the interference and then eliminates it by orthogonal projecting it to the contaminated signal is pro

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Millette, Veronique. "Signal processing of heart signals for the quantification of non-deterministic events." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28579.

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The issue of cavitation in mechanical heart valve (MHV) patients was first recognized when damaged mechanical heart valves were observed. Cavitation bubble implosion can cause mechanical damage to the valve structure and blood elements, when it occurs near the surface of the MHV. Some methods have been suggested to quantify the level of cavitation present in MHV patients. Two algorithms from the literature were selected for implementation and comparison. These algorithms were selected as they have been previously proposed and implemented for in vivo heart signals. In this thesis, a rigorous cl

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4

Mangieri, Eduardo. "An analogue approach for the processing of biomedical signals." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/348009/.

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Constant device scaling has signifcantly boosted electronic systems design in the digital domain enabling incorporation of more functionality within small silicon area and at the same time allows high-speed computation. This trend has been exploited for developing high-performance miniaturised systems in a number of application areas like communication, sensor network, main frame computers, biomedical information processing etc. Although successful, the associated cost comes in the form of high leakage power dissipation and systems reliability. With the increase of customer demands for smarter

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5

Nunes, Neuza Filipa Martins. "Algorithms for time series clustering applied to biomedical signals." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5666.

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Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering<br>The increasing number of biomedical systems and applications for human body understanding creates a need for information extraction tools to use in biosignals. It’s important to comprehend the changes in the biosignal’s morphology over time, as they often contain critical information on the condition of the subject or the status of the experiment. The creation of tools that automatically analyze and extract relevant attributes from biosignals, providing important information to the us

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6

Jayaraman, Vinoth, Sivakumaran Sivalingam, and Sangeetha Munian. "Analysis of Real Time EEG Signals." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-34164.

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The recent evolution in multidisciplinary fields of Engineering, neuroscience, microelectronics, bioengineering and neurophysiology have reduced the gap between human and machine intelligence. Many methods and algorithms have been developed for analysis and classification of bio signals, 1 or 2-dimensional, in time or frequency distribution. The integration of signal processing with the electronic devices serves as a major root for the development of various biomedical applications. There are many ongoing research in this area to constantly improvise and build an efficient human- robotic syste

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Santos, Rui Pedro Silvestre dos. "Time series morphological analysis applied to biomedical signals events detection." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/10227.

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Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical Engineering<br>Automated techniques for biosignal data acquisition and analysis have become increasingly powerful, particularly at the Biomedical Engineering research field. Nevertheless, it is verified the need to improve tools for signal pattern recognition and classification systems, in which the detection of specific events and the automatic signal segmentation are preliminary processing steps. The present dissertation introduces a signal-independent algorithm, which detects significant ev

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8

Lander, P. T. "Computer processing methods for the recovery of low amplitude ECG signals." Thesis, University of Sussex, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.373153.

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9

Vartak, Aniket. "BIOSIGNAL PROCESSING CHALLENGES IN EMOTION RECOGNITIONFOR ADAPTIVE LEARNING." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2667.

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User-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of the cognitive and emotional state of the learner using such systems. This capability moves development

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10

Guilak, Farzin G. "A Spline Framework for Optimal Representation of Semiperiodic Signals." Thesis, Portland State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3722040.

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<p> Semiperiodic signals possess an underlying periodicity, but their constituent spectral components include stochastic elements which make it impossible to analytically determine locations of the signal's critical points. Mathematically, a signal's critical points are those at which it is not differentiable or where its derivative is zero. In some domains they represent characteristic points, which are locations indicating important changes in the underlying process reflected by the signal.</p><p> For many applications in healthcare, knowledge of precise locations of these points provides

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11

CARTA, NICOLA. "Acquisition systems and decoding algorithms of peripheral neural signals for prosthetic applications." Doctoral thesis, Università degli Studi di Cagliari, 2014. http://hdl.handle.net/11584/266473.

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During the years, neuroprosthetic applications have obtained a great deal of attention by the international research, especially in the bioengineering field, thanks to the huge investments on several proposed projects funded by the political institutions which consider the treatment of this particular disease of fundamental importance for the global community. The aim of these projects is to find a possible solution to restore the functionalities lost by a patient subjected to an upper limb amputation trying to develop, according to physiological considerations, a communication link between

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12

Rivolta, M. W. "NON-BLIND SOURCE SEPARATION AND FEATURE EXTRACTION: THEORY, APPROACH AND CASE STUDIES IN CARDIAC SIGNALS." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/278991.

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Source separation (SS) and feature extraction (FE) are tools employed in digital signal processing. The former permits to estimate the values of some sources that have been mixed, and the latter extracts features from a set of measurements. SS and FE are widely applied on biomedical signals such as electrocardiogram (ECG), electroencephalogram, arterial blood pressure, etc., because these signals are collected in noisy environments. For instance, ECG recordings show the electrical activity generated by the whole heart at once. Yet, there are cardiac pathologies or arrhythmias related to only t

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Real, Luiz Fernando Oliveira Corte. "Codificação e compressão iterativa de sinais biomédicos." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-20082013-155137/.

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Em Biomedicina, a detecção e a quanticação de anormalidades presentes num sinal são desejáveis. Uma estratégia de codicação baseada em extração de características, tais como picos ou frequências, pode não capturar todas as irregularidades. Assim, uma representação baseada em funções de base denidas com conhecimento a priori do sinal pode ser mais precisa para aplicações biomédicas. A escolha das funções base depende da natureza siológica do sinal e de suas peculiaridades. Sinais de eletrocardiograma (ECG) e eletroencefalograma (EEG) exibem características bem denidas. ECG, por exemplo, é um s

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Chorão, Ricardo Daniel Domingos. "Parallel programming in biomedical signal processing." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8249.

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Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica<br>Patients with neuromuscular and cardiorespiratory diseases need to be monitored continuously. This constant monitoring gives rise to huge amounts of multivariate data which need to be processed as soon as possible, so that their most relevant features can be extracted. The field of parallel processing, an area from the computational sciences, comes naturally as a way to provide an answer to this problem. For the parallel processing to succeed it is necessary to adapt the pre-existing signal processing algorithms to the mo

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15

Al-Subari, Karema S. A. [Verfasser], Bernd [Akademischer Betreuer] Ludwig, and Elmar Wolfgang [Akademischer Betreuer] Lang. "A Study of Biomedical Time Series Using Empirical Mode Decomposition : Extracting event-related modes from EEG signals recorded during visual processing of contour stimuli / Karema S. A. Al-Subari ; Bernd Ludwig, Elmar Wolfgang Lang." Regensburg : Universitätsbibliothek Regensburg, 2017. http://d-nb.info/1140642073/34.

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Soler, Anderson Ivan Rincon. "Impact of artifact correction methods on R-R interbeat signals to quantifying heart rate variability (HRV) according to linear and nonlinear methods." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-02052016-130306/.

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In the analysis of heart rate variability (HRV) are used temporal series that contains the distances between successive heartbeats in order to assess autonomic regulation of the cardiovascular system. These series are obtained from the electrocardiogram (ECG) signal analysis, which can be affected by different types of artifacts leading to incorrect interpretations in the analysis of the HRV signals. Classic approach to deal with these artifacts implies the use of correction methods, some of them based on interpolation, substitution or statistical techniques. However, there are few studies tha

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17

Mou, Pedro Antonio. "General purpose bioelectric signals acquisition platform combining FPGA and FPAA = 結合FPGA及FPAA的通用生物電信號採集平台". Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2182896.

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Chen, Xun. "Multimodal biomedical signal processing for corticomuscular coupling analysis." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/45811.

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Corticomuscular coupling analysis using multiple data sets such as electroencepha-logram (EEG) and electromyogram (EMG) signals provides a useful tool for understanding human motor control systems. A popular conventional method to assess corticomuscular coupling is the pair-wise magnitude-squared coherence (MSC). However, there are certain limitations associated with MSC, including the difficulty in robustly assessing group inference, only dealing with two types of data sets simultaneously and the biologically implausible assumption of pair-wise interactions. In this thesis, we propose severa

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Palladini, Alessandro <1981&gt. "Statistical methods for biomedical signal analysis and processing." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1358/1/palladini_alessandro_tesi.pdf.

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Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostat

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Palladini, Alessandro <1981&gt. "Statistical methods for biomedical signal analysis and processing." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1358/.

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Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostat

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21

Thonet, Gilles. "New aspects of time-frequency analysis for biomedical signal processing /." Lausanne : EPFL, 1999. http://library.epfl.ch/theses/?nr=1913.

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Dietl, Hubert. "Digital signal processing techniques for detection applied to biomedical data." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419141.

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Nguyen, Quang-Thang. "Contributions to Statistical Signal Processing with Applications in Biomedical Engineering." Télécom Bretagne, 2012. http://www.telecom-bretagne.eu/publications/publication.php?idpublication=13290.

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Cette étude présente des contributions en traitement statistique du signal avec des applications biomédicales. La thèse est divisée en deux parties. La première partie traite de la détection des hotspots à l'interface des protéines. Les hotspots sont les résidus dont les contributions énergétiques sont les plus importantes dans l'interaction entre protéines. Les forêts aléatoires (Random Forests) sont utilisées pour la classification. Une nouvelle famille de descripteurs de hotspot est également introduite. Ces descripteurs sont basés seulement sur la séquence primaire unidimensionnelle d'acid

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Al-Shueli, Assad. "Signal processing for advanced neural recording systems." Thesis, University of Bath, 2013. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.577744.

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Many people around the world suffer from neurological injuries of various sorts that cause serious difficulties in their lives, due to the loss of important sensory and motor functions. Functional electrical stimulation (FES) provides a possible solution to these difficulties by means of a feedback connection allowing the target organ (or organs) to be controlled by electrical stimulation. The control signals can be provided using recorded data extracted from the nerves (electroneurogram, ENG). The most common and safe approaches for interfacing with nerves is called cuff electrodes which deli

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Dia, Nafissa. "Suivi non-invasif du rythme cardiaque foetal : exploitation de la factorisation non-négative des matrices sur signaux électrocardiographiques et phonocardiographiques." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAS034.

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Avec plus de 200 000 naissances par jour dans le monde, la surveillance du bien-être fœtal pendant l'accouchement est un enjeu clinique majeur. Ce suivi se fait au travers du rythme cardiaque fœtal (RCF) et de sa variabilité, et doit être robuste tout en minimisant le nombre de capteurs non-invasifs sur l'abdomen de la mère.Dans ce contexte, les signaux électrocardiogrammes (ECG) et phonocardiogrammes (PCG) sont d’intérêt, puisqu'ils fournissent tous deux des informations cardiaques, à la fois redondantes et complémentaires. Cette multimodalité ainsi que certaines caractéristiques, telles que

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Wang, Zhenzhong. "System Design and Implementation of a Fast and Accurate Bio-Inspired Spiking Neural Network." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2227.

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Neuron models are the elementary units which determine the performance of an artificial spiking neural network (ASNN). This study introduces a new Generalized Leaky Integrate-and-Fire (GLIF) neuron model with variable leaking resistor and bias current in order to reproduce accurately the membrane voltage dynamics of a biological neuron. The accuracy of this model is ensured by adjusting its parameters to the statistical properties of the Hodgkin-Huxley model outputs; while the speed is enhanced by introducing a Generalized Exponential Moving Average method that converts the parameterized kerne

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Murphy, Charuwan. "Signal processing of Doppler ultrasound for the measurement of blood flow." Thesis, University of Oxford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386872.

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Mares, David M. "Developmental laboratories for biomedical instrumentation and digital signal processing with virtual instrument technology and diverse software techniques." Laramie, Wyo. : University of Wyoming, 2006. http://proquest.umi.com/pqdweb?did=1292461511&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Rowley, Alexander. "Signal processing methods for cerebral autoregulation." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:3d85ab53-9c9b-4b50-98f2-2e67848e5da4.

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Cerebral autoregulation describes the clinically observed phenomenon that cerebral blood flow remains relatively constant in healthy human subjects despite large systemic changes in blood pressure, dissolved blood gas concentrations, heart rate and other systemic variables. Cerebral autoregulation is known to be impaired post ischaemic stroke, after severe head injury, in patients suffering from autonomic dysfunction and under the action of various drugs. Cerebral auto-regulation is a dynamic, multivariate phenomenon. Sensitive techniques are required to monitor cerebral auto-regulation in a c

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Ziehe, Andreas. "Blind source separation based on joint diagonalization of matrices with applications in biomedical signal processing." Phd thesis, [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=976710331.

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Schäck, Tim [Verfasser], Abdelhak M. [Akademischer Betreuer] Zoubir, D. Robert [Akademischer Betreuer] Iskander, and Michael [Akademischer Betreuer] Muma. "Photoplethysmography-Based Biomedical Signal Processing / Tim Schäck ; Abdelhak M. Zoubir, D. Robert Iskander, Michael Muma." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2019. http://d-nb.info/1176702009/34.

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Wee, Hoe Cher. "Development of a digital signal processing measurement platform for biomedical magnetic induction tomography and spectroscopy." Thesis, University of South Wales, 2010. https://pure.southwales.ac.uk/en/studentthesis/development-of-a-digital-signal-processing-measurement-platform-for-biomedical-magnetic-induction-tomography-and-spectroscopy(1b717477-e3e6-43ee-9af1-4d3de1d0bee2).html.

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Magnetic Induction Tomography (MIT) is a non-invasive technique that utilises the passive electrical properties of a material to produce cross-sectional images. In MIT system, the signals detected by the sensors must be measured using a phase sensitive technique. A sub-millidegree phase stability is typically required for biological tissues, where the objects to be imaged have relatively low conductivity (< 2S/m). The phase noise and thermal related phase drift in the receiver's signal chain of currently available MIT systems are the major limiting factors of MIT performance for practical m

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Little, M. A. "Biomechanically informed nonlinear speech signal processing." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:6f5b84fb-ab0b-42e1-9ac2-5f6acc9c5b80.

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Linear digital signal processing based around linear, time-invariant systems theory finds substantial application in speech processing. The linear acoustic source-filter theory of speech production provides ready biomechanical justification for using linear techniques. Nonetheless, biomechanical studies surveyed in this thesis display significant nonlinearity and non-Gaussinity, casting doubt on the linear model of speech production. In order therefore to test the appropriateness of linear systems assumptions for speech production, surrogate data techniques can be used. This study uncovers sys

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Li, Kejia. "Wireless reflectance pulse oximeter design and photoplethysmographic signal processing." Thesis, Manhattan, Kan. : Kansas State University, 2010. http://hdl.handle.net/2097/4143.

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Naskovska, Kristina [Verfasser], Martin [Akademischer Betreuer] Haardt, André L. F. de Gutachter] Almeida, and Jens [Gutachter] [Haueisen. "Advanced tensor based signal processing techniques for wireless communication systems and biomedical signal processing / Kristina Naskovska ; Gutachter: André L.F. de Almeida, Jens Haueisen ; Betreuer: Martin Haardt." Ilmenau : TU Ilmenau, 2020. http://d-nb.info/1204004234/34.

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Clifford, Gari D. "Signal processing methods for heart rate variability analysis." Thesis, University of Oxford, 2002. http://ora.ox.ac.uk/objects/uuid:5129701f-1d40-425a-99a3-59a05e8c1b23.

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Heart rate variability (HRV), the changes in the beat-to-beat heart rate calculated from the electrocardiogram (ECG), is a key indicator of an individual's cardiovascular condition. Assessment of HRV has been shown to aid clinical diagnosis and intervention strategies. However, the variety of HRV estimation methods and contradictory reports in this field indicate that there is a need for a more rigorous investigation of these methods as aids to clinical evaluation. This thesis investigates the development of appropriate HRV signal processing techniques in the context of pilot studies in two fi

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Vedala, Krishnatej. "A Novel Signal Processing Method for Intraoperative Neurophysiological Monitoring in Spinal Surgeries." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/1038.

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Intraoperative neurophysiologic monitoring is an integral part of spinal surgeries and involves the recording of somatosensory evoked potentials (SSEP). However, clinical application of IONM still requires anywhere between 200 to 2000 trials to obtain an SSEP signal, which is excessive and introduces a significant delay during surgery to detect a possible neurological damage. The aim of this study is to develop a means to obtain the SSEP using a much less, twelve number of recordings. The preliminary step involved was to distinguish the SSEP with the ongoing brain activity. We first establish

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Chen, Li. "Statistical Machine Learning for Multi-platform Biomedical Data Analysis." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77188.

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Recent advances in biotechnologies have enabled multiplatform and large-scale quantitative measurements of biomedical events. The need to analyze the produced vast amount of imaging and genomic data stimulates various novel applications of statistical machine learning methods in many areas of biomedical research. The main objective is to assist biomedical investigators to better interpret, analyze, and understand the biomedical questions based on the acquired data. Given the computational challenges imposed by these high-dimensional and complex data, machine learning research finds its new opp

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Hong, Jung. "Statistical Parametric Models and Inference for Biomedical Signal Processing: Applications in Speech and Magnetic Resonance Imaging." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10074.

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In this thesis, we develop statistical methods for extracting significant information from biomedical signals. Biomedical signals are not only generated from a complex system but also affected by various random factors during their measurement. The biomedical signals may then be studied in two aspects: observational noise that biomedical signals experience and intrinsic nature that noise-free signals possess. We study Magnetic Resonance (MR) images and speech signals as applications in the one- and two-dimensional signal representation. In MR imaging, we study how observational noise can be ef

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Brennan, Thomas Patrick. "Signal processing methods for characterisation of ventricular repolarisation using the surface electrocardiogram." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:39ae285a-b8dd-4aae-b60e-36f95fb84f37.

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This thesis investigates the mechanisms underlying drug-induced arrhythmia and pro- poses a new approach for the automated analysis of the electrocardiogram (ECG). The current method of assessing the cardiac safety of new drugs in clinical trials is by the measurement and analysis of the QT interval. However, the sensitivity and specificity of the QT interval has been questioned and alternative biomarkers based on T-wave mor- phology have been proposed in the literature. The mechanisms underlying drug effects on T-wave morphology are not clearly understood. Therefore, a combined approach of fo

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Hsu, Ming-Hsuan. "MICROPROCESSOR-COMPATIBLE NEURAL SIGNAL PROCESSING FOR AN IMPLANTABLE NEURODYNAMIC SENSOR." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1244237706.

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Karrakchou, Mohsine. "Advanced techniques for biomedical signal processing and their application for in-vivo measurement of microvascular pulmonary pressure /." Lausanne : EPFL, 1995. http://library.epfl.ch/theses/?nr=1286.

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Gupta, Aditya. "SIGNAL PROCESSING OF AN ECG SIGNAL IN THE PRESENCE OF A STRONG STATIC MAGNETIC FIELD." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2206.

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This dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect diseases such as myocardial infarction. This dissertation aims at finding a solution to the problem of elevati

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Raihani, Nilgoun. "Respiration Pattern Using Amplified Video." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case151272961173245.

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Jansson, Daniel. "Identification Techniques for Mathematical Modeling of the Human Smooth Pursuit System." Doctoral thesis, Uppsala universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-264292.

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This thesis proposes nonlinear system identification techniques for the mathematical modeling of the human smooth pursuit system (SPS) with application to motor symptom quantification in Parkinson's disease (PD). The SPS refers to the complex neuromuscular system in humans that governs the smooth pursuit eye movements (SPEM). Insight into the SPS and its operation is of importance in a wide and steadily expanding array of application areas and research fields. The ultimate purpose of the work in this thesis is to attain a deeper understanding and quantification of the SPS dynamics and thus fac

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Schutter, Christopher Wayne. "Silent Communication Device." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1712.

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Oral communication has constituted as a necessary aspect of how people interact with one another, but there are always situations where this form of communication can create distractions, irritation, or even danger. Take for example, a student in a laboratory who needs to communicate effectively with a lab partner without creating a distraction to those trying to work around said student or a soldier on a battlefield who needs to relay information effectively to his or her comrades without revealing his or her position to the enemy. It becomes apparent that people need a more exclusive form of

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Arichi, Maiko. "Direct Mathematical Method for Real-time Ischemic Detection from Electrocardiograms Using the Discrete Hermite Transform." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1313529081.

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Krishnan, Rajet. "Problems in distributed signal processing in wireless sensor networks." Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1351.

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Gadaleta, Matteo. "Artificial Intelligence for Data Analysis and Signal Processing." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3427321.

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Artificial intelligence, or AI, currently encompasses a huge variety of fields, from areas such as logical reasoning and perception, to specific tasks such as game playing, language processing, theorem proving, and diagnosing diseases. It is clear that systems with human-level intelligence (or even better) would have a huge impact on our everyday lives and on the future course of evolution, as it is already happening in many ways. In this research AI techniques have been introduced and applied in several clinical and real world scenarios, with particular focus on deep learning methods. A

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Sun, Yu. "SIGNAL PROCESSING FOR SHORT WAVE INFRARED (SWIR) RAMAN SPECTROSCOPY DIAGNOSIS OF CANCER." Master's thesis, Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/446864.

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Bioengineering<br>M.S.<br>Raman spectroscopy is an effective optical analysis of the biochemically specific characterization of tissues without contrast agents or exogenous dyes. Applications of Raman spectroscopy include analysis and biomarker investigation, disease diagnosis and surgical guidance. One major challenge in Raman spectroscopy is removing inherent fluorescence background present in samples to acquire Raman signatures. In some tissues, like liver, kidney and darkly pigment skin, the auto-fluorescence background is strong enough to overwhelm the Raman peaks in conventional Near-Inf

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