Дисертації: "Modeling of hydroacoustic signals"

1

Дудко, Андрій Володимирович. "Модуль генерації гідроакустичного сигналу в плоско-паралельному хвилеводі". Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/28408.

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Метою дипломної роботи є створення програмного продукту для генерації гідроакустичного сигналу в плоско-паралельному хвилеводі променевим методом. Об’єктом дослідження є способи та алгоритми моделювання сигналів. Було виконано огляд існуючих програмних застосунків для моделювання сигналів та ознайомитися із проблемами моделювання гідроакустичних сигналів, розроблено програмний продукт для генерації гідроакустичних сигналів, який реалізовано методом уявних джерел для розрахунку поля тиску в плоско-паралельному хвилеводі, даний метод відноситься до променевих моделей. Створена програмний продукт може бути використаний, як частина системи для моделювання гідроакустичних об’єктів та для наукових досліджень. Загальний обсяг роботи: 67 сторінок, 19 ілюстрацій, 17 бібліографічних посилань та 3 додатки.
The purpose of the thesis is to create a program product for generating a hydroacoustic signal in a plane-parallel waveguide beam method. The objects of research are the methods and algorithms of signal simulation. An overview of the existing software applications for simulation of signals and the problems of modeling of hydroacoustic signals was performed, the program software of generation hydroacoustic signals, implemented by the imaginary sources for calculating the field of pressure in a plane-parallel waveguide, was implemented, this method belongs to beam models. The created program product can be used as part of the system for modeling hydroacoustic objects and for scientific research. Total volume of work: 67 pages, 19 illustrations, 17 bibliographic references and 3 attachments.
Целью дипломной работы является создание программного продукта для генерации гидроакустических сигналов в плоско-параллельном волноводе лучевым методом. Объектом исследования являются способы и алгоритмы моделирования сигналов. Было выполнено обзор существующих программных приложений для моделирования сигналов и ознакомиться с проблемами моделирования гидроакустических сигналов, разработано программный продукт для генерации гидроакустических сигналов, который реализован методом мнимых источников для расчета поля давления в плоско-параллельном волноводе, данный метод относится к лучевым моделям. Созданная программа может быть использована как часть системы для моделирования гидроакустических объектов и для научных исследований.

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Тимофеева, М. А., Віктор Васильович Авраменко, Виктор Васильевич Авраменко та Viktor Vasylovych Avramenko. "Разработка компьютерной системы распознавания гидроакустических сигналов и моделирование ее работы". Thesis, Изд-во СумГУ, 2008. http://essuir.sumdu.edu.ua/handle/123456789/20932.

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Разработан алгоритм и компьютерная программа для распознавания судна по текущему значению анализируемого гидроакустического сигнала. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/20932

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Starzl, Ravi. "Computational Modeling of Immune Signals." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/339.

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The primary obstacle to enabling wide spread adoption of composite tissue transplantation, as well as to improving long term solid organ transplant outcomes, is establishing a personalized medication regimen optimizing the balance between immunosuppression and immune function the individual minimum effective level of immunosuppression. Presently, the clinical gold standard for monitoring immune function is histologic inspection of biopsy for tissue damage, or monitoring blood chemistry for signs of organ failure. These trailing indicators reflect damage that has already accumulated, and are of little use in proactively determining the immunologic state of a patient. Samples collected from small animal surgical models were used to quantify the amount of immune signaling protein present (cytokines and chemokines) under various experimental conditions. Patterns in protein expression that reliably discriminate amongst the groups were then investigated with statistical inference methods such as the logistic classifier, decision tree, and random forest, operating in both the original feature space and in transformed feature spaces. This work demonstrates computational methods are effective in elucidating and classifying cytokine profiles, allowing the detection of rejection in composite tissue allografts well in advance of the current clinical gold standard, and shows that the methods can be effective in solid organ contexts as well. This work further determines that cytokine patterns of inflammation associated with rejection are specific to the structure and composition of the tissue in which they occur, and can be distinguished from immune signaling patterns associated with unspecific inflammation, wound healing, or immunosuppressed tissue. Clinical translation of these findings may provide novel computational tools that enable physicians to design personalized immunosuppression strategies for patients. The methods described in this work also provide information that can be used to investigate the biological basis for the observed immune signaling patterns. Further development may provide a computational framework for identifying novel therapeutic strategies in other pathologies.

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4

VanDerKamp, Martha M. "Modeling and classification of biological signals." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School; Available from the National Technical Information Service, 1992. http://edocs.nps.edu/npspubs/scholarly/theses/1992/Dec/92Dec_VanDerKamp.pdf.

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5

Marrow, Marcus. "Detection and modeling of 2-dimensional signals /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3137227.

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6

Xu, Zhen. "Modeling SAR signals and sensors using VHDL." Thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06112009-063128/.

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7

Liu, Aiping. "Brain connectivity network modeling using fMRI signals." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58126.

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Functional magnetic resonance imaging (fMRI) is one of the most popular non-invasive neuroimaging technologies, which examines human brain at relatively good spatial resolution in both normal and disease states. In addition to the investigation of local neural activity in isolated brain regions, brain connectivity estimated from fMRI has provided a system-level view of brain functions. Despite recent progress on brain connectivity inference, there are still several challenges. Specifically, this thesis focuses on developing novel brain connectivity modeling approaches that can deal with particular challenges of real biomedical applications, including group pattern extraction from a population, false discovery rate control, incorporation of prior knowledge and time-varying brain connectivity network modeling. First, we propose a multi-subject, exploratory brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity and determination of the dominant brain connectivity patterns among a group of subjects. Furthermore, to integrate the genetic information at the population level, a framework for genetically-informed group brain connectivity modeling is developed. We then focus on estimating the time-varying brain connectivity networks. The temporal dynamics of brain connectivity assess the brain in the additional temporal dimension and provide a new perspective to the understanding of brain functions. In this thesis, we develop a sticky weighted time-varying model to investigate the time-dependent brain connectivity networks. As the brain must strike a balance between stability and flexibility, purely assuming that brain connectivity is static or dynamic may be unrealistic. We therefore further propose making joint inference of time-invariant connections and time-varying coupling patterns by employing a multitask learning model. The above proposed methods have been applied to real fMRI data sets, and the disease induced changes on the brain connectivity networks have been observed. The brain connectivity study is able to provide deeper insights into neurological diseases, complementing the traditional symptom-based diagnostic methods. Results reported in this thesis suggest that brain connectivity patterns may serve as potential disease biomarkers in Parkinson's Disease.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

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8

Velasco, Solano Carlos Hernando. "ARMA modeling of signals in the time domain." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23820.

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Approved for public release; distribution is unlimited
This thesis develops an iterative algorithm for the design of ARMA models of signals in the time domain. The algorithm is based on optimization techniques, particularly a gradient technique known as the restricted step method is used. The new algorithm is called the iterative Prony method, and the results obtained using this new method are compared to those obtained using the iterative prefiltering algorithm. The thesis shows that the performance of the iterative Prony method is in most of the cases comparable or superior to that of the iterative prefiltering algorithm.

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9

Lam, Warren Michael. "Modeling algorithms for a class of fractal signals." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/31034.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.
Includes bibliographical references (leaves 86-87).
by Warren Michael Lam.
M.S.

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10

Waghray, Rasagnya. "EXPLORING REDUCED TRAFFIC SIGNALS USING AGENT BASED MODELING." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/theses/372.

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The need for measures to reduce congestion in metropolitan traffic has been a pressing concern as citizens' cluster in larger cities with the immediate side effect of an increase in traffic demand. A functioning society depends on the mobility provided by the transportation network to enable its members to participate in essential activities such as production, consumption, communication, and recreation. However, it is necessary for a society to introduce congestion-relief measures for improved quality of life, the environment, and maintained safety of the citizens. The project has three components: 1. An interface, which models a road network and tools to describe data supplied to the network. 2. A simulation interface to observe the model run through time and produce suitable results for the naked streets and to find an improved traffic simulation for the cities. 3. Mathematical modeling for assessment of the pedestrian accident risk and their safety. The traffic is designed and implemented using agent-based modeling (ABM) techniques and I have used NetLogo as my testbed (Wilensky, 2003). Shared Space is not defined by the design or configuration of the environment. Design and detailing are important, but only as a catalyst for changing the way in which people interact within the public spaces. Design standards are not adequate. Even if you follow all design guidelines, it does not guarantee that the space will meet the requirements. Departing from established practice requires determination, careful thought and observation, and the courage to explore and refine novel solutions.

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11

Li, Junning. "Dynamic Bayesian networks : modeling and analysis of neural signals." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/12618.

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Studying interactions between different brain regions or neural components is crucial in understanding neurological disorders. Dynamic Bayesian networks, a type of statistical graphical model, have been suggested as a promising tool to model neural communication systems. This thesis investigates the employment of dynamic Bayesian networks for analyzing neural connectivity, especially with focus on three topics: structural feature extraction, group analysis, and error control in learning network structures. Extracting interpretable features from experimental data is important for clinical diagnosis and improving experiment design. A framework is designed for discovering structural differences, such as the pattern of sub-networks, between two groups of Bayesian networks. The framework consists of three components: Bayesian network modeling, statistical structure-comparison, and structure-based classification. In a study on stroke using surface electromyography, this method detected several coordination patterns among muscles that could effectively differentiate patients from healthy people. Group analyses are widely conducted in neurological research. However for dynamic Bayesian networks, the performances of different group-analysis methods had not been systematically investigated. To provide guidance on selecting group-analysis methods, three popular methods, i.e. the virtual-typical-subject, the common-structure and the individual-structure methods, were compared in a study on Parkinson's disease, from the aspects of their statistical goodness-of-fit to the data, and more importantly, their sensitivity in detecting the effect of medication. The three methods led to considerably different group-level results, and the individual-structure approach was more sensitive to the normalizing effect of medication. Controlling errors is a fundamental problem in applying dynamic Bayesian networks to discovering neural connectivity. An algorithm is developed for this purpose, particularly for controlling the false discovery rate (FDR). It is proved that the algorithm is able to curb the FDR under user-specified levels (for example, conventionally 5%) at the limit of large sample size, and meanwhile recover all the true connections with probability one. Several extensions are also developed, including a heuristic modification for moderate sample sizes, an adaption to prior knowledge, and a combination with Bayesian inference.

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Herschke, Philippe M. "Modeling and extrapolation of path delays in GPS signals." Zurich : ETH, Swiss Federal Institute of Technology, Department of Physics, 2002. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=90.

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13

Arafat, Samer M. "Uncertainty modeling for classification and analysis of medical signals /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3115520.

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14

Bertolini, Daniele. "Comparison of EEG signals from epilepsy patients with Bump modeling." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5067/.

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Background: l’epilessia è una malattia cerebrale che colpisce oggigiorno circa l’1% della popolazione mondiale e causa, a chi ne soffre, convulsioni ricorrenti e improvvise che danneggiano la vita quotidiana del paziente. Le convulsioni sono degli eventi che bloccano istantaneamente la normale attività cerebrale; inoltre differiscono tra i pazienti e, perciò, non esiste un trattamento comune generalizzato. Solitamente, medici neurologi somministrano farmaci, e, in rari casi, l’epilessia è trattata con operazioni neurochirurgiche. Tuttavia, le operazioni hanno effetti positivi nel ridurre le crisi, ma raramente riescono a eliminarle del tutto. Negli ultimi anni, nel campo della ricerca scientifica è stato provato che il segnale EEG contiene informazioni utili per diagnosticare l'arrivo di un attacco epilettico. Inoltre, diversi algoritmi automatici sono stati sviluppati per rilevare automaticamente le crisi epilettiche. Scopo: lo scopo finale di questa ricerca è l'applicabilità e l'affidabilità di un dispositivo automatico portatile in grado di rilevare le convulsioni e utilizzabile come sistema di monitoraggio. L’analisi condotta in questo progetto, è eseguita con tecniche di misure classiche e avanzate, in modo tale da provare tecnicamente l’affidabilità di un tale sistema. La comparazione è stata eseguita sui segnali elettroencefalografici utilizzando due diversi sistemi di acquisizione EEG: il metodo standard utilizzato nelle cliniche e il nuovo dispositivo portatile. Metodi: è necessaria una solida validazione dei segnali EEG registrati con il nuovo dispositivo. I segnali saranno trattati con tecniche classiche e avanzate. Dopo le operazioni di pulizia e allineamento, verrà utilizzato un nuovo metodo di rappresentazione e confronto di segnali : Bump model. In questa tesi il metodo citato verrà ampiamente descritto, testato, validato e adattato alle esigenze del progetto. Questo modello è definito come un approccio economico per la mappatura spazio-frequenziale di wavelet; in particolare, saranno presenti solo gli eventi con un’alta quantità di energia. Risultati: il modello Bump è stato implementato come toolbox su MATLAB dallo sviluppatore F. Vialatte, e migliorato dall’Autore per l’utilizzo di registrazioni EEG da sistemi diversi. Il metodo è validato con segnali artificiali al fine di garantire l’affidabilità, inoltre, è utilizzato su segnali EEG processati e allineati, che contengono eventi epilettici. Questo serve per rilevare la somiglianza dei due sistemi di acquisizione. Conclusioni: i risultati visivi garantiscono la somiglianza tra i due sistemi, questa differenza la si può notare specialmente comparando i grafici di attività background EEG e quelli di artefatti o eventi epilettici. Bump model è uno strumento affidabile per questa applicazione, e potrebbe essere utilizzato anche per lavori futuri (ad esempio utilizzare il metodo di Sincronicità Eventi Stocas- tici SES) o differenti applicazioni, così come le informazioni estratte dai Bump model potrebbero servire come input per misure di sincronicità, dalle quali estrarre utili risultati.

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15

Costen, Fumie. "High speed computational modeling in the application of UWB signals." 京都大学 (Kyoto University), 2005. http://hdl.handle.net/2433/144793.

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16

Ozawa, Koji, Toshihiro Wakita, Chiyomi Miyajima, Katsunobu Itou, and Kazuya Takeda. "MODELING OF INDIVIDUALITIES IN DRIVING THROUGH SPECTRAL ANALYSIS OF BEHAVIORAL SIGNALS." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2005. http://hdl.handle.net/2237/10456.

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17

Liu, Aiping. "FDR-controlled network modeling and analysis of fMRI and sEMG signals." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/37217.

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Neural recording technologies such as functional magnetic resonance imaging (fMRI) and surface electroencephalography (sEMG) provide great potential to studying the underlying neural systems and the related diseases. A broad range of statistical methods have been developed to model interactions between neural components. In this thesis, a false discovery rate (FDR)-controlled exploratory group modeling approach is introduced to model interaction/cooperation between neural components. Group network modeling for comparison between populations is of great common interest in biomedical signal processing, particularly when there might be considerable heterogeneity within one or more groups, such as disease populations. A group-level network modeling process, the group PCfdr algorithm with taking into account inter-subject variances, is proposed. The group PCfdr algorithm combines group inference with a graphical modeling approach for discovering statistically significant structure connectivity. Simulation results demonstrate that the group PCfdr algorithm can accurately recover the underlying group network structures and robustly control the FDR at user-specified levels. To further extract informative features and compare the connectivity patterns across groups at the network level, network analysis methods including graph theoretical analysis, lesion and perturbation analysis are applied to examine the inferred networks. It can provide great potential to investigate the connectivity patterns as well as the particular changes associated with certain disease states. The proposed network modeling and analysis approach is applied to fMRI data collected from control and Parkinson's Disease (PD) groups. The network analysis results of the PD groups before and after L-dopa medication support the hypothesis that PD subjects could be ameliorated by the medication. In addition, based on the comparison between PD subtypes, we observe that the learned brain effective networks across PD subtypes display different connectivity patterns. In another sEMG study in low back pain, significant findings of muscle coordination networks are found to be associated with low back pain. The results indicate that the networks representing the normal group clearly exhibit globally symmetrical patterns between the left and right sEMG channels, while the connections between sEMG channels for the patient group are more likely to cluster locally and the learned group networks show the loss of global symmetrical patterns.

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Dzhambazov, Georgi. "Knowledge-based probabilistic modeling for tracking lyrics in music audio signals." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/404681.

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This thesis proposes specific signal processing and machine learning methodologies for automatically aligning the lyrics of a song to its corresponding audio recording. The research carried out falls in the broader field of music information retrieval (MIR) and in this respect, we aim at improving some existing state-of-the-art methodologies, by introducing domain-specific knowledge. The goal of this work is to devise models capable of tracking in the music audio signal the sequential aspect of one particular element of lyrics - the phonemes. Music can be understood as comprising different facets, one of which is lyrics. The models we build take into account the complementary context that exists around lyrics, which is any musical facet complementary to lyrics. The facets used in this thesis include the structure of the music composition, structure of a melodic phrase, the structure of a metrical cycle. From this perspective, we analyse not only the low-level acoustic characteristics, representing the timbre of the phonemes, but also higher-level characteristics, in which the complementary context manifests. We propose specific probabilistic models to represent how the transitions between consecutive sung phonemes are conditioned by different facets of complementary context. The complementary context, which we address, unfolds in time according to principles that are particular of a music tradition. To capture these, we created corpora and datasets for two music traditions, which have a rich set of such principles: Ottoman Turkish makam and Beijing opera. The datasets and the corpora comprise different data types: audio recordings, music scores, and metadata. From this perspective, the proposed models can take advantage both of the data and the music-domain knowledge of particular musical styles to improve existing baseline approaches. As a baseline, we choose a phonetic recognizer based on hidden Markov models (HMM): a widely-used methodology for tracking phonemes both in singing and speech processing problems. We present refinements in the typical steps of existing phonetic recognizer approaches, tailored towards the characteristics of the studied music traditions. On top of the refined baseline, we device probabilistic models, based on dynamic Bayesian networks (DBN) that represent the relation of phoneme transitions to its complementary context. Two separate models are built for two granularities of complementary context: the structure of a melodic phrase (higher-level) and the structure of the metrical cycle (finer-level). In one model we exploit the fact the syllable durations depend on their position within a melodic phrase. Information about the melodic phrases is obtained from the score, as well as from music-specific knowledge.Then in another model, we analyse how vocal note onsets, estimated from audio recordings, influence the transitions between consecutive vowels and consonants. We also propose how to detect the time positions of vocal note onsets in melodic phrases by tracking simultaneously the positions in a metrical cycle (i.e. metrical accents). In order to evaluate the potential of the proposed models, we use the lyrics-to-audio alignment as a concrete task. Each model improves the alignment accuracy, compared to the baseline, which is based solely on the acoustics of the phonetic timbre. This validates our hypothesis that knowledge of complementary context is an important stepping stone for computationally tracking lyrics, especially in the challenging case of singing with instrumental accompaniment. The outcomes of this study are not only theoretic methodologies and data, but also specific software tools that have been integrated into Dunya - a suite of tools, built in the context of CompMusic, a project for advancing the computational analysis of the world's music. With this application, we have also shown that the developed methodologies are useful not only for tracking lyrics, but also for other use cases, such as enriched music listening and appreciation, or for educational purposes.
La tesi aquí presentada proposa metodologies d’aprenentatge automàtic i processament de senyal per alinear automàticament el text d’una cançó amb el seu corresponent enregistrament d’àudio. La recerca duta a terme s’engloba en l’ampli camp de l’extracció d’informació musical (Music Information Retrieval o MIR). Dins aquest context la tesi pretén millorar algunes de les metodologies d’última generació del camp introduint coneixement específic de l’àmbit. L’objectiu d’aquest treball és dissenyar models que siguin capaços de detectar en la senyal d’àudio l’aspecte seqüencial d’un element particular dels textos musicals; els fonemes. Podem entendre la música com la composició de diversos elements entre els quals podem trobar el text. Els models que construïm tenen en compte el context complementari del text. El context són tots aquells aspectes musicals que complementen el text, dels quals hem utilitzat en aquest tesi: la estructura de la composició musical, la estructura de les frases melòdiques i els accents rítmics. Des d’aquesta prespectiva analitzem no només les característiques acústiques de baix nivell, que representen el timbre musical dels fonemes, sinó també les característiques d’alt nivell en les quals es fa patent el context complementari. En aquest treball proposem models probabilístics específics que representen com les transicions entre fonemes consecutius de veu cantanda es veuen afectats per diversos aspectes del context complementari. El context complementari que tractem aquí es desenvolupa en el temps en funció de les característiques particulars de cada tradició musical. Per tal de modelar aquestes característiques hem creat corpus i conjunts de dades de dues tradicions musicals que presenten una gran riquesa en aquest aspectes; la música de l’opera de Beijing i la música makam turc-otomana. Les dades són de diversos tipus; enregistraments d’àudio, partitures musicals i metadades. Des d’aquesta prespectiva els models proposats poden aprofitar-se tant de les dades en si mateixes com del coneixement específic de la tradició musical per a millorar els resultats de referència actuals. Com a resultat de referència prenem un reconeixedor de fonemes basat en models ocults de Markov (Hidden Markov Models o HMM), una metodologia abastament emprada per a detectar fonemes tant en la veu cantada com en la parlada. Presentem millores en els processos comuns dels reconeixedors de fonemes actuals, ajustant-los a les característiques de les tradicions musicals estudiades. A més de millorar els resultats de referència també dissenyem models probabilistics basats en xarxes dinàmiques de Bayes (Dynamic Bayesian Networks o DBN) que respresenten la relació entre la transició dels fonemes i el context complementari. Hem creat dos models diferents per dos aspectes del context complementari; la estructura de la frase melòdica (alt nivell) i la estructura mètrica (nivell subtil). En un dels models explotem el fet que la duració de les síl·labes depén de la seva posició en la frase melòdica. Obtenim aquesta informació sobre les frases musical de la partitura i del coneixement específic de la tradició musical. En l’altre model analitzem com els atacs de les notes vocals, estimats directament dels enregistraments d’àudio, influencien les transicions entre vocals i consonants consecutives. A més també proposem com detectar les posicions temporals dels atacs de les notes en les frases melòdiques a base de localitzar simultàniament els accents en un cicle mètric musical. Per tal d’evaluar el potencial dels mètodes proposats utlitzem la tasca específica d’alineament de text amb àudio. Cada model proposat millora la precisió de l’alineament en comparació als resultats de referència, que es basen exclusivament en les característiques acústiques tímbriques dels fonemes. D’aquesta manera validem la nostra hipòtesi de que el coneixement del context complementari ajuda a la detecció automàtica de text musical, especialment en el cas de veu cantada amb acompanyament instrumental. Els resultats d’aquest treball no consisteixen només en metodologies teòriques i dades, sinó també en eines programàtiques específiques que han sigut integrades a Dunya, un paquet d’eines creat en el context del projecte de recerca CompMusic, l’objectiu del qual és promoure l’anàlisi computacional de les músiques del món. Gràcies a aquestes eines demostrem també que les metodologies desenvolupades es poden fer servir per a altres aplicacions en el context de la educació musical o la escolta musical enriquida.

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Lu, Yinghui. "Analysis and modeling of diffuse ultrasonic signals for structural health monitoring." Diss., Available online, Georgia Institute of Technology, 2007, 2007. http://etd.gatech.edu/theses/available/etd-07052007-225427/.

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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.
Durgin, Gregory, Committee Member ; Vachtsevanos, George, Committee Member ; Michaels, Thomas, Committee Member ; Michaels, Jennifer, Committee Chair ; Jacobs, Laurence, Committee Member.

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20

Rajalingham, Rishi. "Characterization, modeling and analysis of neural signals for brain-machine interface applications." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110553.

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Part 1: Brain-machine interfaces provide a means of communication between the brain and the environment by extracting and decoding neural signals for control of external devices such as prosthetic limbs. Although such devices are promising solutions to help paralyzed patients, progress is limited by the quantity of information that can be safely extracted from the brain. The strategy proposed in this project is to maximize the amount of information recorded from a minimum number of electrodes by increasing the number of independent modalities recorded. We recorded local oxygen concentration using a fluorescence-based optical sensor, simultaneously with the electrical activity of single neurons in the posterior parietal cortex of Rhesus macaque monkeys. Local oxygen was measured using a fluorescence-quenching optical oxygen sensor, while electrical neural signals were recorded with a microelectrode. Throughout the recordings, monkeys performed a delayed memory reach task to targets in the 2D fronto-parallel plane. We characterized the acquired signal and its relationship to spikes and local field potentials. Results showed that local oxygen increased from baseline during the memory period, independently of the recorded neural activity. Additionally, this modulation in oxygen concentration was used in conjunction with spiking and LFP activity to decode reach directions. Class-dependent information was measured on all combinations of the recorded modalities, using a cross-validated classification scheme. We found that oxygen contains complementary information to spikes and LFPs which arises early in the trial., This early predictive signal would be a useful input for cognitive brain-machine interfaces.Part 2:Previous research has shown that the expected value of reward associated with a reach modulates neural activity in the medial intraparietal cortex (MIP). In this study, we investigated the temporal dynamics of reward modulation in MIP by examining how given reward magnitudes are encoded when presented in different contexts or schedules. We recorded neural activity while monkeys performed a delayed reach task under two reward schedules. In the variable schedule, an equal number of small and large rewards were randomly interleaved trial by trial, while in the constant schedule only one of these rewards was delivered for a block of trials. Each recording session consisted of a block of trials with a variable schedule as well as blocks with both small and large constant schedules. Neural activity for the same reward was observed to vary significantly between the two schedules. Specifically, the discrimination between large and small reward at the neural level was significantly less in the variable reward schedule than in the constant reward schedule. This result is accounted for by the dependence of instantaneous firing rate on past trials, which is shown using information theoretic metrics. We modeled the neural firing rate as a linear system response to reward using a systems identification approach, and showed that the modeled systems effectively low-pass filtered reward signals. Given this observation, we hypothesize that this filtering mechanism leads to a robust memory of low-risk rewards, advantageous to decision-making mechanisms in the brain that assess rewards against risks.
Part 1: Les interfaces cerveau-machine propose un moyen de communication entre le cerveau et l'environnement en extrayant et en décodant les signaux neuraux. Ces signaux peuvent ensuite être utilisés pour actionner un appareil externe comme un membre artificiel. Même si ces appareils sont une solution prometteuse pour les gens atteints de paralysie, les progrès sont limités par la quantité d'information qui peut-être extraite en toute sécurité. La stratégie proposée par ce projet est de maximiser la quantité d'information échantillonnée d'un minimum d'électrode, en augmentant le nombre de modalité. Nous avons enregistré simultanément la concentration locale d'oxygène, en utilisant un senseur optique basé sur la fluorescence, et l'activité électrochimique d'un seul neurone dans le cortex pariétal postérieur d'un singe macaque Rhésus. L'oxygène locale a été mesurée en utilisant un senseur optique d'oxygène à extinction de fluorescence, alors que l'activité électrochimique a été enregistrée à l'aide d'une microélectrode. Au cours de l'échantillonnage, les singes ont performé une tâche de mémorisation d'atteinte de cible à délai, la cible étant présentée dans un plan 2D situé en face du singe. Nous avons caractérisé le signal acquis et sa relation avec les impulsions du neurone et le champ de potentiel locale. Les résultats montrent que l'oxygène locale a augmenté par rapport à son niveau de référence durant la période de mémorisation indépendamment de l'activité neuronale enregistrée. De plus, cette modulation de la concentration d'oxygène a été utilisée en conjonction avec les impulsions des neurones et le champ de potentiel local pour décoder la direction du mouvement. L'information donnée en fonction de la classe a été mesurée pour toute les combinaisons de modalités enregistrées, en utilisant une méthode de classification par validation croisée. Nous avons trouvé que l'oxygène contient de l'information complémentaire à celle convoyée par les impulsions et le champ de potentiel local, en plus d'être disponible plus tôt au cours de l'essai. Part 2: Les études précédentes ont démontré que la valeur de la récompense anticipée associé avec un mouvement module l'activité neuronale dans l'aire intrapariétale médial (IPM). Dans notre étude, nous examinons la dynamique temporelle de la modulation par la récompense dans l'aire IPM en questionnant comment la magnitude de la récompense est encodée lorsque présentée dans différent contexte ou planning. Nous avons enregistré l'activité des neurones alors que les singes performaient une tâche d'atteinte de cible à délai sous deux conditions de récompenses. Dans la condition variable, un nombre égal d'essai résultant en de petite ou large récompenses ont été entrelacés de façon aléatoire, alors que pour la condition constante, un seul format de récompense était dispensé pour la durée d'un bloc d'essai. Chaque séance d'enregistrement a consisté en un bloc d'essai à condition variable et de blocs à conditions constantes dispensant une petite et une large récompense. L'activité neuronale pour la même récompense a varié de façon significative entre les deux conditions. Spécifiquement, la différence dans le taux d'impulsions entre la petite et la large récompense pour la condition variable était plus petite que pour la condition de récompense constante. Ces résultats indiquent que la récompense par essai n'est pas indépendante, au niveau neuronale, mais plutôt que l'activité des neurones est modulé par la récompense présente et passée. En utilisant l'identification de système, nous avons modélisé les impulsions en fonction de la récompense comme un système linéaire. Nous avons observé que le système modélisé effectuait un filtrage à passe-bas du signal de récompense. Nous défendons l'hypothèse que ce mécanisme entraine une mémoire robuste pour les récompenses de bas risque, avantageux pour les mécanismes de prise de décision du cerveau qui compare la récompense attendu au risque encouru.

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Weiss, Jan Peter. "Modeling and characterization of multipath in global navigation satellite system ranging signals." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3284495.

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22

Yamazaki, Ken'ichi. "Study on methods for regional geomagnetic field modeling to detect tectonomagnetic signals." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/144196.

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Kyoto University (京都大学)
0048
新制・課程博士
博士(理学)
甲第12113号
理博第3007号
新制||理||1448(附属図書館)
23949
UT51-2006-J108
京都大学大学院理学研究科地球惑星科学専攻
(主査)教授大志万直人, 教授田中良和, 教授橋本学
学位規則第4条第1項該当

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23

Jenior, Peter M. "Observation and Modeling of Traffic Operations at Intersections in Malfunction Flash Mode." Thesis, Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14643.

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When a traffic signals malfunction monitoring unit detects a problem with a traffic signal such as the simultaneous display of green indications to conflicting movements or loss of power to some signal heads, the signal is automatically placed into flash mode as a safety precaution. Signals can have either red/red malfunction flash mode or yellow/red malfunction flash mode, and the mode cannot change by time of day or day of week. This study analyzed traffic operation at 34 instances of yellow/red malfunction flash and 9 instances of red/red malfunction flash in the Atlanta, Georgia area. Many of these instances were during high volume periods. A high level of driver confusion exists at malfunction flash intersections. The rate at which through major street drivers (i.e. those facing a flashing yellow signal) stopped exceeded 75 percent at some yellow/red flash intersections. This creates a safety hazard for other major street drivers who are not expecting vehicles to stop, and for minor street drivers who cannot tell what type of control is being presented to cross traffic or do not understand that vehicles are not required to stop when approaching a flashing yellow indication. Furthermore, high stopping rates at a flashing yellow signal eliminate many of the operational benefits that yellow/red flash is assumed to have over red/red flash. Based on the findings of this study, the use of red/red flash should be the primary flash mode and possibly used exclusively. Requiring all vehicles to stop will improve safety conditions and not have large operational impacts at intersections where a majority of major street vehicles are already stopping at a flashing yellow signal. Yellow/red flash may be an acceptable malfunction flash mode at the intersection of a very large street and a very small street, but additional measures would be required at these intersections to address potential driver confusion.

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24

Yeo, Gene W. (Gene Wei-Ming) 1977. "Identification, improved modeling and integration of signals to predict constitutive and altering splicing." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28853.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2004.
Includes bibliographical references.
(cont.) manipulation of intronic elements that enables fish genes to be spliced properly in mammalian cells; (iii) A computational analysis using EST data, genome sequence data, and microarray expression data of tissue- specific alternative splicing is conducted, which distinguishes human brain, testis and liver as having unusually high levels of AS, highlights differences in the types of AS occurring commonly in different tissues, and identifies candidate cis-regulatory elements and trans-factors likely to play important roles in tissue-specific AS in human cells; (iv) The identification of a set of discriminatory sequence features and their integration into a statistical machine-learning algorithm, ACEScan, which distinguishes exons subject to evolutionarily conserved alternative splicing from constitutively spliced or lineage-specifically-spliced exons is described; (v) The genome-wide search for and experimental validation of exon-skipping events using the combination of two silencing cis-elements, UAGG and GGGG.
The regulation of pre-messenger RNA splicing by the spliceosomal machinery via interactions between cis-regulatory elements and splicing trans-factors to generate a specific mRNA i.e. constitutive splicing, or sometimes many distinct mRNA isoforms i.e. alternative splicing, is still a poorly understood process. Progress into illuminating this process is further exacerbated by the variation of splicing in the multitude of tissues and cell types present, as well as the variation of cis and trans elements in different organisms, and the possibility that some alternative splicing events present in expressed sequence tag (EST) databases may constitute biochemical 'noise' or transient evolutionary fluctuations. Several studies, mainly computational in nature, addressing different questions regarding constitutive and alternative splicing are described here, ranging from improved modeling of splicing signals, studying the variation of alternative splicing in various tissues, analyzing evolutionary differences of cis and trans elements of splicing in various vertebrates, and utilizing attributes indicative of alternative splicing events conserved in human and mouse to identify novel alternatively spliced exons. In particular: (i) A general approach for improved modeling of short sequence motifs, based on the Maximum Entropy principle, that incorporates local adjacent and non-adjacent position dependencies is introduced, and applied to understanding splice site signals. The splice site recognition algorithm, MaxENTScan, performs better than previous models that utilize as input similar length sequences; (ii) The first large-scale bioinformatics study is conducted that identifies similarities and differences in candidate cis-regulatory elements and trans-acting splicing
by Gene W. Yeo.
Ph.D.

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25

Mouawad, Pauline. "Modeling and predicting affect in audio signals : perspectives from acoustics and chaotic dynamics." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0627/document.

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La présente thèse décrit un projet de recherche multidisciplinaire qui porte sur la reconnaissance de l’émotion dans les sons, couvrant les théories psychologiques, l’analyse du signal acoustique,l’apprentissage automatique et la dynamique chaotique.Dans nos interactions et nos relations sociales, nous dépendons considérablement de la communication de l’information et de notre perception des messages transmis. En fait, la communication se produit lorsque les signaux transmettent des informations entre une source et une destination. Le signal peut être verbal, et l’information est ensuite portée par des motifs sonores, tels que des mots. Dans la communication vocale non verbale, cependant,l’information peut être des modèles perceptifs qui véhiculent des indices affectifs, que nous percevons et évaluons sous la forme d’intentions, d’attitudes, d’humeurs et d’émotions.La prévalence de la composante affective peut être observée dans les interactions informatiques humaines (HCI) où le développement d’applications automatisées qui comprennent et expriment les émotions est devenu crucial. De tels systèmes doivent être significatifs et faciles à utiliser pour l’utilisateur final, de sorte que notre interaction avec eux devient une expérience positive. Bien que la reconnaissance automatique des émotions dans les sons ait reçu une attention accrue au cours des dernières années, il s’agit encore d’un jeune domaine de recherche.Non seulement cela contribue à l’informatique affective en général, mais il fournit également une compréhension approfondie de la signification des sons dans notre vie quotidienne.Dans cette thèse, le problème de la reconnaissance des affects est abordé à partir d’une double perspective: nous commençons par adopter une approche standard de l’analyse acoustique du signal, où nous examinons et expérimentons les fonctionnalités existantes pour déterminer leur rôle dans la communication émotionnelle. Ensuite, nous nous tournons vers la dynamique chaotique et la symbolisation des séries temporelles, pour comprendre le rôle de la dynamique inhérente des sons dans l’expressivité affective. Nous menons nos études dans le contexte des sons non verbaux, à savoir les sons vocaux, musicaux et environnementaux.D’un point de vue de l’écoute humaine, une tâche d’annotation est menée pour construire un ground-truth de voix de chant non verbales, marquées par des descriptions catégoriques du modèle bidimensionnel d’émotions. Deux types de sons sont inclus dans l’étude: vocal et glottal.D’un point de vue psychologique, la présente recherche porte sur un débat qui existe depuis longtemps parmi les scientifiques et les psychologues, concernant les origines communes de la musique et de la voix. La question est abordée à partir d’une analyse acoustique ainsi que d’une approche dynamique non linéaire.D’un point de vue de la modélisation, ce travail propose une nouvelle approche dynamique non linéaire pour la reconnaissance de l’affect dans le son, basée sur la dynamique chaotique et la symbolisation adaptative des séries temporelles. Tout au long de cette thèse, les contrastes clés dans l’expressivité de l’émotion sont illustrés parmi les différents types de sons, à travers l’analyse des propriétés acoustiques, les métriques de la dynamique non linéaire et les performances des prédictions.Enfin, d’un point de vue progressif, nous suggérons que les travaux futurs étudient des caractéristiques motivées par les études cognitives. Nous suggérons également d’examiner dans quelle mesure nos caractéristiques reflètent les processus cognitifs. En outre, nous recommandons que nos fonctionnalités dynamiques soient testées dans des études à grande échelle de la reconnaissance d’émotions à travers la participation aux défis expérimentaux, dans le but de vérifier s’ils obtiennent un consensus
The present thesis describes a multidisciplinary research project on emotion recognition in sounds, covering psychological theories, acoustic-based signal analysis, machine learning and chaotic dynamics.In our social interactions and relationships, we rely greatly on the communication of information and on our perception of the messages transmitted. In fact communication happens when signals transmit information between a source and a destination. The signal can be verbal,and the information is then carried by sound patterns, such as words. In non verbal vocal communication however, information can be perceptual patterns that convey affective cues,that we sense and appraise in the form of intentions, attitudes, moods and emotions.The prevalence of the affective component can be seen in human computer interactions(HCI) where the development of automated applications that understand and express emotions has become crucial. Such systems need to be meaningful and friendly to the end user, so thatour interaction with them becomes a positive experience. Although the automatic recognition of emotions in sounds has received increased attention in recent years, it is still a young fieldof research. Not only does it contribute to Affective Computing in general, but it also provides insight into the significance of sounds in our daily life.In this thesis the problem of affect recognition is addressed from a dual perspective: we start by taking a standard approach of acoustic-based signal analysis, where we survey and experiment with existing features to determine their role in emotion communication. Then,we turn to chaotic dynamics and time series symbolization, to understand the role of the inherent dynamics of sounds in affective expressiveness. We conduct our studies in the context of nonverbal sounds, namely voice, music and environmental sounds.From a human listening point of view, an annotation task is conducted to build a ground truth of nonverbal singing voices, labelled with categorical descriptions of the two-dimensional model of affect. Two types of sounds are included in the study: vocal and glottal.From a psychological perspective, the present research addresses a debate that is of long standing among scientists and psychologists, concerning the common origins of music and voice.The question is addressed from an acoustic-based analysis as well as a nonlinear dynamics approach.From a modeling viewpoint, this work proposes a novel nonlinear dynamics approach for the recognition of affect in sound, based on chaotic dynamics and adaptive time series symbolization.Throughout this thesis, key contrasts in the expressiveness of affect are illustrated among the different types of sounds, through the analysis of acoustic properties, nonlinear dynamics metrics and predictions performances. Finally from a progressive perspective, we suggest that future works investigate features that are motivated by cognitive studies. We also suggest to examine to what extent our features reflect cognitive processes. Additionally we recommend that our dynamic features be tested inlarge scale ER studies through the participation in ER challenges, with an aim to verify if they gain consensus

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26

Acharya, Abhishek. "Modeling of oversaturated conditions on arterial networks." Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2008m/acharya.pdf.

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27

Redyuk, Sergey. "Finding early signals of emerging trends in text through topic modeling and anomaly detection." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15507.

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Trend prediction has become an extremely popular practice in many industrial sectors and academia. It is beneficial for strategic planning and decision making, and facilitates exploring new research directions that are not yet matured. To anticipate future trends in academic environment, a researcher needs to analyze an extensive amount of literature and scientific publications, and gain expertise in the particular research domain. This approach is time-consuming and extremely complicated due to abundance of data and its diversity. Modern machine learning tools, on the other hand, are capable of processing tremendous volumes of data, reaching the real-time human-level performance for various applications. Achieving high performance in unsupervised prediction of emerging trends in text can indicate promising directions for future research and potentially lead to breakthrough discoveries in any field of science. This thesis addresses the problem of emerging trend prediction in text in two main steps: it utilizes HDP topic model to represent latent topic space of a given temporal collection of documents, DBSCAN clustering algorithm to detect groups with high-density regions in the document space potentially leading to emerging trends, and applies KLdivergence in order to capture deviating text which might indicate birth of a new not-yet-seen phenomenon. In order to empirically evaluate the effectiveness of the proposed framework and estimate its predictive capability, both synthetically generated corpora and real-world text collections from arXiv.org, an open-access electronic archive of scientific publications (category: Computer Science), and NIPS publications are used. For synthetic data, a text generator is designed which provides ground truth to evaluate the performance of anomaly detection algorithms. This work contributes to the body of knowledge in the area of emerging trend prediction in several ways. First of all, the method of incorporating topic modeling and anomaly detection algorithms for emerging trend prediction is a novel approach and highlights new perspectives in the subject area. Secondly, the three-level word-document-topic topology of anomalies is formalized in order to detect anomalies in temporal text collections which might lead to emerging trends. Finally, a framework for unsupervised detection of early signals of emerging trends in text is designed. The framework captures new vocabulary, documents with deviating word/topic distribution, and drifts in latent topic space as three main indicators of a novel phenomenon to occur, in accordance with the three-level topology of anomalies. The framework is not limited by particular sources of data and can be applied to any temporal text collections in combination with any online methods for soft clustering.

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Shachykov, Andrii. "Neural modeling of human motor coordination inspired by biological signals aiming for parkinsonian gaits." Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0291.

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Cette thèse présente une plate-forme de simulation neuro-musculo-squelettique du système locomoteur humain pour reproduire des allures de marche saines ou altérées par la maladie de Parkinson, ou par d’autres troubles locomoteurs. Le premier chapitre s’intéresse aux grands principes du système locomoteur en se focalisant sur les structures neuronales du cerveau qui sont le siège des troubles parkinsoniens. La transmission à la moelle épinière des signaux de contrôle de l'activité musculaire au travers de plusieurs boucles fermées est décrite. Différents modèles neuronaux des troubles parkinsoniens issus de la littérature sont présentés. Le second chapitre présente le contrôleur neuronal implémenté dans la plate-forme. Il utilise un modèle original de «central pattern generators» (CPG) inspiré du réseau locomoteur spinal. Ce CPG peut générer des signaux rythmiques variables selon ses paramètres neuronaux contrôlés par des signaux descendants du cerveau. Les signaux des motoneurones du CPG sont appliqués en tant qu'excitation au modèle de muscles flexeur/extenseur. Le chapitre trois présente les simulateurs musculo-squelettiques GAIT2DE et OpenSim utilisés ainsi que les modifications apportées pour simuler, en boucle fermée, le système locomoteur marchant sur le sol et les retours proprioceptifs et extéroceptifs exploités par les CPGs. Le chapitre quatre concerne l'analyse du cycle de la marche et l'optimisation des paramètres du contrôleur. Le cycle de marche permet de comparer des données de simulation avec des paramètres de marche réelle, et d’optimiser le contrôleur à partir d'une analyse comparative utilisant la corrélation croisée. Le chapitre cinq présente les résultats obtenus avec les deux simulateurs en intégrant une circuiterie complète à base des CPGs et d’un réflexe du contrôle d’équilibre. Les résultats montrent qu’on peut générer différentes démarches plus ou moins coordonnées selon les paramètres neuronaux reproduisant ainsi les allures observées pour la maladie de Parkinson ou d’autres troubles connus en médecine. Le dernier chapitre conclu et propose certaines améliorations de la plate-forme dans son ensemble pour simuler des démarches dues à d’autres maladies neurodégénératives ou à l’impact de prothèses ou suite à des interventions chirurgicales
My thesis aims to simulate the impact of motor disorders on the human gait to help non-invasive diagnosis of neurodegenerative diseases such as Parkinson's disease. Indeed, the simulation of the human locomotor system helps to deepen our understanding of the functioning of the human body by providing biological, biomechanical and kinematic data that would be difficult to collect otherwise and by helping to evaluate the coordination of a patient's movements to predict its condition after surgery. The goal of my thesis is, more specifically, to create a new platform for neuro-musculoskeletal simulation of the human locomotor system to reproduce healthy or altered walking gaits by Parkinson's disease or by disorders of the musculoskeletal system or locomotor disorders. The work presented includes several matters. Firstly, the main principles of the nervous system that control human locomotion are reviewed, by focusing on neural structures located in the brain and which are the sources of parkinsonian disorders. The neural controller of the simulation platform is based on an original model of central pattern generator (CPG) inspired by the spinal locomotor network and developed at LORIA in recent years. The musculoskeletal simulators are used in this thesis to obtain a closed-loop physical simulation of the locomotor system walking on the ground and whose proprioceptive and exteroceptive sensory feedback is used by the CPGs. The musculoskeletal simulator GAIT2DE was used with the OpenSim simulator which is more realistic and more used in Biomechanics field. The simulated gait analysis and controller parameter optimization are concerned followed by the results obtained with the simulators. These results show that it is possible to generate different walking patterns that are relatively stable and coordinated by modifying the neuronal parameters of GPCs. The simulation platform will allow to simulate abnormal gait due to different causes such as neurodegenerative diseases or the impact of the addition of artificial limbs (prostheses) and surgical interventions

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29

Lutsyk, Nadiia. "Modeling and methods of biomechanical heart signals processing using the conditional cyclic random process." Thesis, Clermont-Ferrand 2, 2016. http://www.theses.fr/2016CLF22726.

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Ce travail a été réalisé en cotutelle entre l'Université Nationale de Technologie de Ternopil Ivan Pul'uj (TNTU, Ukraine) et l’Université Blaise Pascal (France). Il appartient au domaine scientifique de la biomécanique et de l'informatique. Le but de l'étude est de développer les modèles et les méthodes de traitement des signaux biomécaniques cardiaques par les systèmes de diagnostic assisté par ordinateur avec une précision accrue, informativité et de la complexité de calcul inférieure. La méthode d'analyse statistique du rythme cardiaque a été mise au point. Cette méthode possède une plus grande précision et informativité par rapport aux méthodes connues d'analyse du rythme cardiaque. Dans cette thèse, le logiciel existant de l'analyse des signaux cardiaques biomécaniques a été améliorée par l'ajout de nouveaux modules logiciels, qui mettent en œuvre les nouvelles méthodes de l'analyse du rythme cardiaque et de l'analyse morphologique des signaux cardiaques biomécaniques
This work has been performed under the co-tutelle agreement between Ternopil Ivan Pul’uj National Technical University in Ternopil (TNTU, Ukraine) and the University Blaise Pascal in Clermont-Ferrand (France). It belongs to the scientific field of biomechanics and informatics. The aim of the study is to develop the mathematical models and methods of the processing of biomechanical heart signals in computer-based diagnostic systems with increased accuracy, informativeness and lower computational complexity. The method of statistical analysis of heart rhythm was developed, which is characterised by higher accuracy and informativeness compared with the known methods of heart rhythm analysis. In this thesis, the existing software of the analysis of biomechanical heart signals was improved by means of adding new software modules that implement the new methods of the analysis of heart rhythm and morphologic analysis of biomechanical heart signals

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30

Ivonin, Leonid I. "Digitizing arquetypal human expereience through physiological signals." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/668915.

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The problem of capturing human experience is relevant in many application domains. In fact, the process of describing and sharing individual experience lies at the heart of human culture. This advancement came at a price of losing some of the multidimensional aspects of primary, bodily experience during its projection into the symbolic formThroughout the courses of our lives we learn a great deal of information about the world from other people's experience. Besides the ability to share utilitarian experience such as whether a particular plant is poisonous, humans have developed a sophisticated competency of social signaling that enables us to express and decode emotional experience. The natural way of sharing emotional experiences requires those who share to be co-present during this event. However, people have overcome the limitation of physical presence by creating a symbolic system of representations.Recent research in the field of affective computing has addressed the question of digitization and transmission of emotional experience through monitoring and interpretation of physiological signals. Although the outcomes of this research represent a great step forward in developing a technology that supports sharing of emotional experiences, they do not seem to help in preserving the original phenomenological experience during the aforementioned projection. This circumstance is explained by the fact that in affective computing the focus of investigation has been aimed at emotional experiences which can be consciously evaluated and described by individuals themselves. Therefore, generally speaking, applying an affective computing technique for capturing emotions of an individual is not a deeper or more precise way to project her experience into the symbolic form than asking this person to write down a description of her emotions on a piece of paper. One can say that so far the research in affective computing has aimed at delivering technology that could automate the projection but it has not considered the problem of improving the projection in order to preserve more of the multidimensional aspects of human experience.This dissertation examines whether human experience, which individuals are not able to consciously transpose into the symbolic representation, can still be captured using the techniques of affective computing.First, a theoretical framework for description of human experience which is not accessible for conscious awareness was formulated. This framework was based on the work of Carl Jung who introduced a model of a psyche that includes three levels: consciousness, the personal unconscious and the collective unconscious. Consciousness is the external layer of the psyche that consists of those thoughts and emotions which are available for one¿s conscious recollection. The personal unconscious represents a repository for all of an individual¿s feelings, memories, knowledge and thoughts that are not conscious at a given moment of time.The collective unconscious is a repository of universal modes and behaviors that are similar in all individuals. According to Jung, the collective unconscious is populated with archetypes. Archetypes are prototypical categories of objects, people and situations that existed across evolutionary time and in different cultures.
Esta tesis doctoral examina si la experiencia humana, que los individuos no pueden transponer conscientemente a la representación simbólica, aún puede capturarse utilizando las técnicas de computación afectiva. Primero, se formula un marco teórico para la descripción de la experiencia humana que no es accesible para la conciencia consciente. Este marco se basó en el trabajo de Carl Jung, quien introdujo un modelo de psique que incluye tres niveles: la conciencia, el inconsciente personal y el inconsciente colectivo. Habiendo definido nuestro marco teórico, realizamos un experimento en el que se mostraron a los sujetos estímulos visuales y auditivos de bases de datos estandarizadas para la obtención de emociones conscientes. Aparte de los estímulos para las emociones conscientes, los sujetos fueron expuestos a estímulos que representaban el arquetipo del yo. Durante la presentación de los estímulos cardiovasculares se registraron las señales de los sujetos. Los resultados experimentales indicaron que las respuestas de la frecuencia cardíaca de los participantes fueron únicas para cada categoría de estímulos, incluido el arquetípico. Estos hallazgos dieron impulso a realizar otro estudio en el que se examinó un espectro más amplio de experiencias arquetípicas. En nuestro segundo estudio, hicimos un cambio de estímulos visuales y auditivos a estímulos audiovisuales porque se esperaba que los videos fueran más eficientes en la obtención de emociones conscientes y experiencias arquetípicas que las imágenes fijas o los sonidos. La cantidad de arquetipos aumentó y los sujetos en general fueron estimulados a sentir ocho experiencias arquetípicas diferentes. También preparamos estímulos para emociones conscientes. En este experimento, las señales fisiológicas incluyeron actividades cardiovasculares, electrodérmicas, respiratorias y temperatura de la piel. El análisis estadístico sugirió que las experiencias arquetípicas podrían diferenciarse en función de las activaciones fisiológicas. Además, se construyeron varios modelos de predicción basados en los datos fisiológicos recopilados. Estos modelos demostraron la capacidad de clasificar los arquetipos con una precisión que era considerablemente más alta que el nivel de probabilidad. Como los resultados del segundo estudio sugirieron una relación positiva entre las experiencias arquetípicas y las activaciones de señales fisiológicas, parecía razonable realizar otro estudio para confirmar la generalización de nuestros hallazgos. Sin embargo, antes de comenzar un nuevo experimento, se decidió construir una herramienta que pudiera facilitar la recopilación de datos fisiológicos y el reconocimiento de experiencias arquetípicas, así como de emociones conscientes. Tal herramienta nos ayudaría a nosotros y a otros investigadores a realizar experimentos sobre la experiencia humana. Nuestra herramienta funciona en "tablets" y admite la recopilación y el análisis de datos de sensores fisiológicos. El último estudio se realizó utilizando una metodología similar al segundo experimento con varias modificaciones que tenían como objetivo obtener resultados más sólidos. El esfuerzo de realizar este estudio se redujo considerablemente al usar la herramienta desarrollada. Durante el experimento, sólo medimos las actividades cardiovasculares y electrodérmicas de los sujetos porque nuestros experimentos anteriores mostraron que estas dos señales contribuyeron significativamente a la clasificación de las emociones conscientes y las experiencias arquetípicas. El análisis estadístico indicó una relación significativa entre los arquetipos retratados en los videos y las respuestas fisiológicas de los sujetos. Además, utilizando métodos de minería de datos, creamos modelos de predicción que fueron capaces de reconocer las experiencias arquetípicas con una precisión menor que en el segundo estudio, pero todavía considerablemente...

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31

Jones, Benjamin A. "Acoustic scattering of broadband echolocation signals from prey of Blainville's beaked whales : modeling and analysis /." Thesis, Online version, 2006. http://hdl.handle.net/1912/1283.

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Thesis (M.S.)--Joint Program in Oceanography/ Applied Ocean Science and Engineering, Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution, 2006.
"September 2006." Bibliography: p. 89-96.

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32

Bian, Linkan. "Stochastic modeling and prognostic analysis of complex systems using condition-based real-time sensor signals." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51753.

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This dissertation presents a stochastic framework for modeling the degradation processes of components in complex engineering systems using sensor based signals. Chapters 1 and 2 discuses the challenges and the existing literature in monitoring and predicting the performance of complex engineering systems. Chapter 3 presents the degradation model with the absorbing failure threshold for a single unit and the RLD estimation using the first-passage-time approach. Subsequently, we develop the estimate of the RLD using the first-passage-time approach for two cases: information prior distributions and non-informative prior distributions. A case study is presented using real-world data from rolling elements bearing applications. Chapter 4 presents a stochastic methodology for modeling degradation signals from components functioning under dynamically evolving environmental conditions. We utilize in-situ sensor signals related to the degradation process, as well as the environmental conditions, to predict and continuously update, in real-time, the distribution of a component’s residual lifetime. Two distinct models are presented. The first considers future environmental profiles that evolve in a deterministic manner while the second assumes the environment evolves as a continuous-time Markov chain. Chapters 5 and 6 generalize the failure-dependent models and develop a general model that examines the interactions among the degradation processes of interconnected components/subsystems. In particular, we model how the degradation level of one component affects the degradation rates of other components in the system. Hereafter, we refer to this type of component-to-component interaction caused by their stochastic dependence as degradation-rate-interaction (DRI). Chapter 5 focuses on the scenario in which these changes occur in a discrete manner, whereas, Chapter 6 focuses on the scenario, in which DRIs occur in a continuous manner. We demonstrate that incorporating the effects of component interactions significantly improves the prediction accuracy of RLDs. Finally, we outline the conclusion remarks and a future work plan in Chapter 7.

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33

May, Gary L. "Pole-zero modeling of transient waveforms : a comparison of methods with application to acoustic signals." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/28454.

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34

Niu, Li. "Studies of imbalance difference theory in modeling conversion between differential mode and common mode signals." Thesis, Clemson University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3680740.

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This dissertation describes three related studies regarding the imbalance difference theory in modeling the conversion between differential mode and common mode/antenna mode signals. The topics covered are: rigorous derivation of imbalance difference theory for modeling radiated emission problems, modeling the conversion between differential mode and common mode propagation in transmission lines, and modeling the loading impedance on differential mode signals due to radiated emissions.

The imbalance difference theory describes a method for calculating the coupling between differential mode signals and common mode signals due to changes in electrical balance on a transmission line. It provides both physical insight and a simple technique for modeling the conversions between the two modes.

The first chapter presents a rigorous derivation of imbalance difference theory for modeling radiated emission problems. Although the theory has been successfully used to model a wide variety of important EMC problems over the past, it has not been rigorously derived. The derivation carefully defines the important quantities and demonstrates that imbalance difference calculations are exact provided that the differential-mode propagation is TEM and the current division factor, h, represents the actual ratio of currents on the two transmission line conductors excited by a common-mode source. This chapter also discusses the acquisition of the current division factor from 2D calculations of the cross-section of the transmission line.

The second chapter provides a rigorous development of the imbalance difference theory for three-conductor transmission lines where both the differential mode and common mode exhibit TEM propagation. It also derives expressions for the mode conversion impedances, which account for the energy converted from one mode to the other. They are essential for modeling the conversion between the two modes when they are strongly coupled.

The third chapter introduces conversion impedance to the existing imbalance difference theory model for modeling radiated emission problems, so that when the coupling between differential mode and antenna mode are strong, the imbalance difference theory can more accurately estimate the antenna mode current.

All three papers are about confirming, enriching and expanding the imbalance difference theory. The first chapter focuses on the rigorous derivation of theory for its most common application, radiated emission problems. The second chapter expands the theory to multi-conductor transmission line structure when the two modes are strongly coupled. The last chapter introduces conversion impedance to the theory in modeling radiated emission problems and improves the accuracy of the model at resonant frequencies.

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35

Jones, Benjamin A. (Benjamin Aaron). "Acoustic scattering of broadband echolocation signals from prey of Blainville's beaked whales : modeling and analysis." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/39228.

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Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 89-96).
Blainville's beaked whales (Mesoplodon densirostris) use broadband, ultrasonic echolocation signals (27 to 57 kHz) to search for, localize, and approach prey that generally consist of mid-water and deep-water fishes and squid. Although it is well known that the spectral characteristics of broadband echoes from marine organisms are a strong function of size, shape, orientation and anatomical group, little is known as to whether or not these or other toothed whales use spectral cues in discriminating between prey and non-prey. In order to study the prey-classification process, a stereo acoustic tag was mounted on a Blainville's beaked whale so that emitted clicks and corresponding echoes from prey could be recorded. A comparison of echoes from prey selected by the whale and those from randomly chosen scatterers suggests that the whale may have, indeed, discriminated between echoes using spectral features and target strengths. Specifically, the whale appears to have favored prey with one or more deep nulls in the echo spectra as well as ones with higher target strength. A three-dimensional, acoustic scattering model is also developed to simulate broadband scattering from squid, a likely prey of the beaked whale.
(cont.) This model applies the distorted wave Born approximation (DWBA) to a weakly-scattering, inhomogeneous body using a combined ray trace and volume integration approach. Scatterer features are represented with volume elements that are small (less than 1=12th of the wavelength) for the frequency range of interest (0 to 120 kHz). Ranges of validity with respect to material properties and numerical considerations are explored using benchmark computations with simpler geometries such as fluid-filled spherical and cylindrical fluid shells. Modeling predictions are compared with published data from live, freely swimming squid. These results, as well as previously published studies, are used in the analysis of the echo spectra of the whale's ensonified targets.
by Benjamin A. Jones.
S.M.

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36

Mészáros, Tomáš. "Speech Analysis for Processing of Musical Signals." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234974.

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Hlavním cílem této práce je obohatit hudební signály charakteristikami lidské řeči. Práce zahrnuje tvorbu audioefektu inspirovaného efektem talk-box: analýzu hlasového ústrojí vhodným algoritmem jako je lineární predikce, a aplikaci odhadnutého filtru na hudební audio-signál. Důraz je kladen na dokonalou kvalitu výstupu, malou latenci a nízkou výpočetní náročnost pro použití v reálném čase. Výstupem práce je softwarový plugin využitelný v profesionálních aplikacích pro úpravu audia a při využití vhodné hardwarové platformy také pro živé hraní. Plugin emuluje reálné zařízení typu talk-box a poskytuje podobnou kvalitu výstupu s unikátním zvukem.

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37

GAMA, ANTONIO LOPES. "MODELING OF PIEZOELECTRIC ELEMENTS FOR EXCITATION AND SENSING OF HIGH FREQUENCY ACOUSTIC SIGNALS IN COMPOSITE BEAMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1998. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26579@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
O problema tratado nesta dissertação é o da modelagem da excitação, propagação e sensoriamento de ondas acústicas de alta frequência em compósitos piezoelétricos laminados. Empregou-se uma formulação de vigas baseada na teoria discreta de Reddy para compósitos laminados. Esta teoria tem como característica o emprego de funções de interpolação de ordem arbitrária para descrever a variação dos campos de deslocamento e do potencial elétrico ao longo da espessura do laminado. A utilização deste tipo de teoria possibilita a representação de campos de deslocamento que variam rapidamente ao longo da espessura, e que na faixa de altas frequências, são representativos daqueles associados com ondas guiadas em compósitos laminados. No domínio da frequência, o sistema de equações que descreve a resposta eletromecânica do laminado piezoelétrico é colocado na forma de uma equação de estado. A técnica de solução utilizada é baseada no método da imersão invariante, também conhecido como método da varredura ou método de Riccati. Para avaliar os limites da faixa de frequências onde diferentes aproximações são válidas, empregou-se uma técnica baseada na comparação entre o espectro de dispersão exato e aquele previsto pela teoria aproximada. Os resultados destas comparações, mostram que utilizando-se o número de funções de interpolação apropriado, a teoria discreta de Reddy é capaz de representar ondas de comprimento até quatro vezes menores que a espessura da viga. Na faixa de baixas frequências, os resultados são comparados com soluções numéricas fornecidas pelo método dos elementos finitos. Também são apresentados resultados que mostram a variação na resposta eletro-mecânica da viga quando descontinuidades são introduzidas nesta, bem como resultados de experimentos que envolvem a utilização de piezoelétricos como atuadores e sensores.
The proposed contribution adresses the problem of modeling high- frequency excitation, propagation, and sensing, of structural waves in composites containing piezoelectric sensors and actuators. The model for the active composite beam proposed here is based on Reddy’s discrete layerwise laminate theory. The displacement and electric potential are interpolated in the through-thickness direction using piecewise linear functions. In the frequency domain, the governing equations are written in a state space form. Comparisons between approximate and exact wave dispersion spectra are performed in order to assess the efficiency of the layerwise theory in a given frequency range. It is shown that by employing the proper interpolation, Reddy s theory is able to describe the dynamic response of the composite at frequencies where the associated wavelengths are of the same order or even shorter than the thickness of the piezoelectric layers. Next, the solution for the state space equation is obtained by employing an algorithm based on a discrete version of the Riccati transformation, It is shown that the algorithm is stable over a wide range of frequencies. In the low frequency range, the analytical results are compared with finite element results. Also are shown the change in the electrical and mechanical response when discontinuites are introduced in the beam, as well as basic experiments involving piezoelectric sensors and actuators.

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38

Muvvala, Priyanka. "Feasibility of Troposphere Propagation Delay Modeling of GPS Signals using Three-Dimensional Weather Radar Reflectivity Returns." Ohio University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1307041652.

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39

Ximing, LI. "Insights into Delivery of Somatic Calcium Signals to the Nucleus During LTP Revealed by Computational Modeling." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou152236301476345.

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40

Milo, Michael William. "Anomaly Detection in Heterogeneous Data Environments with Applications to Mechanical Engineering Signals & Systems." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23962.

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Anomaly detection is a relevant problem in the field of Mechanical Engineering, because the analysis of mechanical systems often relies on identifying deviations from what is considered "normal". The mechanical sciences are represented by a heterogeneous collection of data types: some systems may be highly dimensional, may contain exclusively spatial or temporal data, may be spatiotemporally linked, or may be non-deterministic and best described probabilistically. Given the broad range of data types in this field, it is not possible to propose a single processing method that will be appropriate, or even usable, for all data types. This has led to human observation remaining a common, albeit costly and inefficient, approach to detecting anomalous signals or patterns in mechanical data. The advantages of automated anomaly detection in mechanical systems include reduced monitoring costs, increased reliability of fault detection, and improved safety for users and operators. This dissertation proposes a hierarchical framework for anomaly detection through machine learning, and applies it to three distinct and heterogeneous data types: state-based data, parameter-driven data, and spatiotemporal sensor network data. In time-series data, anomaly detection results were robust in synthetic data generated using multiple simulation algorithms, as well as experimental data from rolling element bearings, with highly accurate detection rates (>99% detection, <1% false alarm). Significant developments were shown in parameter-driven data by reducing the sample sizes necessary for analysis, as well as reducing the time required for computation. The event-space model extends previous work into a geospatial sensor network and demonstrates applications of this type of event modeling at various timescales, and compares the model to results obtained using other approaches. Each data type is processed in a unique way relative to the others, but all are fitted to the same hierarchical structure for system modeling. This hierarchical model is the key development proposed by this dissertation, and makes both novel and significant contributions to the fields of mechanical analysis and data processing. This work demonstrates the effectiveness of the developed approaches, details how they differ from other relevant industry standard methods, and concludes with a proposal for additional research into other data types.
Ph. D.

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41

Abd-Elrady, Emad. "Nonlinear Approaches to Periodic Signal Modeling." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4644.

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42

Schneck-Scott, Adria R. "Detection and resolvability of pulsed acoustic signals through the South China Sea Basin : a A modeling analysis /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Sep%5FSchneck%5FScott.pdf.

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Thesis (M.S. in Physical Oceanography)--Naval Postgraduate School, September 2005.
Thesis Advisor(s): Ching-Sang Chiu, Chris Miller, John Joseph. Includes bibliographical references (p. 27-28). Also available online.

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43

Alkhatib, Rami. "Human locomotion analysis, classification and modeling of normal and pathological vertical ground reaction force signals in elderly." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES029/document.

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La marche est définie par des séquences de gestes cycliques et répétées. Il a été déjà montré que la vitesse et la variabilité de ces séquences peuvent révéler des aptitudes ou des défaillances motrices. L’originalité de ce travail est alors d’analyser et de caractériser les foulées de sujets âgés à partir des signaux de pression issus de semelles instrumentées lors de la marche, au moyen d’outils de traitement du signal. Une étude préliminaire, sur les signaux de pression générés lors de la marche, nous a permis de mettre en évidence le caractère cyclo-stationnaire de ces signaux. Ces paramètres sont testées sur une population de 47 sujets. Tout d'abord, nous avons commencé par un prétraitement des signaux et nous avons montré dans la première de cette thèse que le filtrage peut éliminer une partie vitale du signal. C’est pourquoi un filtre adaptatif basé sur la décomposition en mode empirique a été conçu. Les points de retournement ont été filtrés ensuite en utilisant une technique temps-fréquence appelée «synochronosqueezing». Nous avons également montré que le contenu des signaux de force de marche est fortement affecté par des paramètres inquantifiables tels que les tâches cognitives qui les rendent difficiles à normaliser. C’est pourquoi les paramètres extraits de nos signaux sont tous dérivées par une comparaison inter-sujet. Par exemple, nous avons assimilé la différence dans la répartition de poids entre les pieds. Il est également recommandé dans ce travail de choisir le centre des capteurs plutôt que de compter sur la somme des forces issues du réseau de capteurs pour la classification. Ensuite, on a montré que l’hypothèse de la marche équilibrée et déséquilibrée peut améliorer les résultats de la classification. Le potentiel de cette hypothèse est montré à l'aide de la répartition du poids ainsi que le produit de l'âge × vitesse dans le premier classificateur et la corrélation dans le second classificateur. Une simulation de la série temporelle de VGRF basé sur une version modifiée du modèle de Markov non stationnaire, du premier ordre est ensuite dérivée. Ce modèle prédit les allures chez les sujets normaux et suffisamment pour les allures des sujets de Parkinson. On a trouvé que les trois modes: temps, fréquence et espace sont très utiles pour l’analyse des signaux de force, c’est pourquoi l’analyse de facteurs parallèles est introduite comme étant une méthode de tenseur qui peut être utilisée dans le futur
Walking is defined as sequences of repetitive cyclic gestures. It was already shown that the speed and the variability of these sequences can reveal abilities or motorskill failures. The originality of this work is to analyze and characterize the steps of elderly persons by using pressure signals. In a preliminary study, we showed that pressure signals are characterized by cyclostationarity. In this study, we intend to exploit the nonstationarity of the signals in a search for new indicators that can help in gait signal classification between normal and Parkinson subjects in the elderly population. These parameters are tested on a population of 47 subjects. First, we started with preprocessing the vertical ground reaction force (VGRF) signals and showed in this first part of the thesis that filtering can remove a vital part of the signal. That is why an adaptive filter based on empirical mode decomposition (EMD) was built. Turning points are filtered using synochronosqueezing of time-frequency representations of the signal. We also showed that the content of gait force signals is highly affected by unquantifiable parameter such as cognitive tasks which make them hard to be normalized. That is why features being extracted are derived from inter-subject comparison. For example we equated the difference in the load distribution between feet. It is also recommended in this work to choose the mid-sensor rather than relying on summation of forces from array of sensors for classification purposes. A hypothesis of balanced and unbalanced gait is verified to be potential in improving the classification accuracy. The power of this hypothesis is shown by using the load distribution and Age×Speed in the first classifier and the correlation in the second classifier. A time series simulation of VGRF based on a modified version of nonstationary- Markov model of first order is derived. This model successfully predict gaits in normal subjects and fairly did in Parkinson’s gait. We found out that the three modes: time, frequency and space are helpful in analyzing force signals that is why parallel factor analysis is introduced as a tensor method to be used in a future work

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44

Mayor, Torres Juan Manuel. "Modeling Heart and Brain signals in the context of Wellbeing and Autism Applications: A Deep Learning Approach." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/247209.

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The analysis and understanding of physiological and brain signals is critical in order to decode user’s behavioral/neural outcome measures in different domain scenarios. Personal Health-Care agents have been proposed recently in order to monitor and acquire reliable data from daily activities to enhance control participants’ wellbeing, and the quality of life of multiple non-neurotypical participants in clinical lab-controlled studies. The inclusion of new wearable devices with increased and more compact memory requirements,and the possibility to include long-size datasets on the cloud and network-based applications agile the implementation of new improved computational health-care agents. These new enhanced agents are able to provide services including real time health-care,medical monitoring, and multiple biological outcome measures-based alarms for medicaldoctor diagnosis. In this dissertation we will focus on multiple Signal Processing (SP), Machine Learning (ML), Saliency Relevance Maps (SRM) techniques and classifiers with the purpose to enhance the Personal Health-care agents in a multimodal clinical environment. Therefore, we propose the evaluation of current state-of-the-art methods to evaluate the incidence of successful hypertension detection, categorical and emotion stimuli decoding using biosignals. To evaluate the performance of ML, SP, and SRM techniques proposed in this study, wedivide this thesis document in two main implementations: 1) Four different initial pipelines where we evaluate the SP, and ML methodologies included here for an enhanced a) Hypertension detection based on Blood-Volume-Pulse signal (BVP) and Photoplethysmography (PPG) wearable sensors, b) Heart-Rate (HR) and Inter-beat-interval (IBI) prediction using light adaptive filtering for physical exercise/real environments, c) Object Category stimuli decoding using EEG features and features subspace transformations, and d) Emotion recognition using EEG features from recognized datasets. And 2) A complete performance and robust SRM evaluation of a neural-based Emotion Decoding/Recognition pipeline using EEG features from Autism Spectrum Disorder (ASD) groups. This pipeline is presented as a novel assistive system for lab-controlled Face Emotion Recognition (FER) intervention ASD subjects. In this pipeline we include a Deep ConvNet asthe Deep classifier to extract the correct neural information and decode emotions successfully.

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45

Kossakowska, Diana Elizabeth [Verfasser], and Thomas [Akademischer Betreuer] Henning. "Modeling and determining origins of signals found in radial velocity data / Diana Elizabeth Kossakowska ; Betreuer: Thomas Henning." Heidelberg : Universitätsbibliothek Heidelberg, 2021. http://nbn-resolving.de/urn:nbn:de:bsz:16-heidok-304316.

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46

Fernandez, Raul 1971. "Stochastic modeling of physiological signals with hidden Markov models : a step toward frustration detection in human-computer interfaces." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47436.

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47

Eltrass, Ahmed Said Hassan Ahmed. "The Mid-Latitude Ionosphere: Modeling and Analysis of Plasma Wave Irregularities and the Potential Impact on GPS Signals." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/51804.

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The mid-latitude ionosphere is more complicated than previously thought, as it includes many different scales of wave-like structures. Recent studies reveal that the mid-latitude ionospheric irregularities are less understood due to lack of models and observations that can explain the characteristics of the observed wave structures. Since temperature and density gradients are a persistent feature in the mid-latitude ionosphere near the plasmapause, the drift mode growth rate at short wavelengths may explain the mid-latitude decameter-scale ionospheric irregularities observed by the Super Dual Auroral Radar Network (SuperDARN). In the context of this dissertation, we focus on investigating the plasma waves responsible for the mid-latitude ionospheric irregularities and studying their influence on Global Positioning System (GPS) scintillations. First, the physical mechanism of the Temperature Gradient Instability (TGI), which is a strong candidate for producing mid-latitude irregularities, is proposed. The electro- static dispersion relation for TGI is extended into the kinetic regime appropriate for High- Frequency (HF) radars by including Landau damping, finite gyro-radius effects, and tem- perature anisotropy. The kinetic dispersion relation of the Gradient Drift Instability (GDI) including finite ion gyro-radius effects is also solved to consider decameter-scale waves gen- eration. The TGI and GDI calculations are obtained over a broad set of parameter regimes to underscore limitations in fluid theory for short wavelengths and to provide perspective on the experimental observations. Joint measurements by the Millstone Hill Incoherent Scatter Radar (ISR) and the Su- perDARN HF radar located at Wallops Island, Virginia have identified the presence of decameter-scale electron density irregularities that have been proposed to be responsible for low-velocity Sub-Auroral Ionospheric Scatter (SAIS) observed by SuperDARN radars. In order to investigate the mechanism responsible for the growth of these irregularities, a time series for the growth rate of both TGI and GDI is developed. The time series is computed for both perpendicular and meridional density and temperature gradients. The growth rate comparison shows that the TGI is the most likely generation mechanism for the observed quiet-time irregularities and the GDI is expected to play a relatively minor role in irregular- ity generation. This is the first experimental confirmation that mid-latitude decameter-scale ionospheric irregularities are produced by the TGI or by turbulent cascade from primary irregularity structures produced from this instability. The quiet- and disturbed-times plasma wave irregularities are compared by investigating co-located experimental observations by the Blackstone SuperDARN radar and the Millstone Hill ISR under various sets of geomagnetic conditions. The radar observations in conjunction with growth rate calculations suggest that the TGI in association with the GDI or a cascade product from them may cause the observations of disturbed-time sub-auroral ionospheric irregularities. Following this, the nonlinear evolution of the TGI is investigated utilizing gyro-kinetic Particle-In-Cell (PIC) simulation techniques with Monte Carlo collisions for the first time. The purpose of this investigation is to identify the mechanism responsible for the nonlinear saturation as well as the associated anomalous transport. The simulation results indicate that the nonlinear E x B convection (trapping) of the electrons is the dominant TGI sat- uration mechanism. The spatial power spectra of the electrostatic potential and density fluctuations associated with the TGI are also computed and the results show wave cascad- ing of TGI from kilometer scales into the decameter-scale regime of the radar observations. This suggests that the observed mid-latitude decameter-scale ionospheric irregularities may be produced directly by the TGI or by turbulent cascade from primary longer-wavelength irregularity structures produced from this instability. Finally, the potential impact of the mid-latitude ionospheric irregularities on GPS signals is investigated utilizing modeling and observations. The recorded GPS data at mid-latitude stations are analyzed to study the amplitude and phase fluctuations of the GPS signals and to investigate the spectral index variations due to ionospheric irregularities. The GPS measurements show weak to moderate scintillations of GPS L1 signals in the presence of ionospheric irregularities during disturbed geomagnetic conditions. The GPS spectral indices are calculated and found to be in the same range of the numerical simulations of TGI and GDI. Both simulation results and GPS spectral analysis are consistent with previous in-situ satellite measurements during disturbed periods, showing that the spectral index of mid- latitude density irregularities are of the order 2. The scintillation results along with radar observations suggest that the observed decameter-scale irregularities that cause SuperDARN backscatter, co-exist with kilometer-scale irregularities that cause L-band scintillations. The alignment between the experimental, theoretical, and computational results of this study suggests that turbulent cascade processes of TGI and GDI may cause the observations of GPS scintillations that occur under disturbed conditions of the mid-latitude F-region ionosphere. The TGI and GDI wave cascading lends further support to the belief that the E-region may be responsible for shorting out the F-region TGI and GDI electric fields before and around sunset and ultimately leading to irregularity suppression.
Ph. D.

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48

Dunai, Larisa. "Design, modeling and analysis of object localization through acoustical signals for cognitive electronic travel aid for blind people." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/8441.

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El objetivo de la tesis consiste en el estudio y análisis de la localización de objetos en el entorno real mediante sonidos, así como la posterior integración y ensayo de un dispositivo real basado en tal técnica y destinado a personas con discapacidad visual. Con el propósito de poder comprender y analizar la localización de objetos se ha realizado un profundo estado de arte sobre los Sistemas de Navegación desarrollados durante las últimas décadas y orientados a personas con distintos grados de discapacidad visual. En el citado estado del arte, se han analizado y estructurado los dispositivos de navegación existentes, clasificándolos de acuerdo con los componentes de adquisición de datos del entorno utilizados. A este respecto, hay que señalar que, hasta el momento, se conocen tres clases de dispositivos de navegación: 'detectores de obstáculos', que se basan en dispositivos de ultrasonidos y sensores instalados en los dispositivos electrónicos de navegación con el objetivo de detectar los objetos que aparecen en el área de trabajo del sistema; 'sensores del entorno' - que tienen como objetivo la detección del objeto y del usuario. Esta clase de dispositivos se instalan en las estaciones de autobús, metro, tren, pasos de peatones etc., de forma que cuando el sensor del usuario penetra en el área de alcance de los sensores instalados en la estación, éstos informan al usuario sobre la presencia de la misma. Asimismo, el sensor del usuario detecta también los medios de transporte que tienen instalado el correspondiente dispositivo basado en láser o ultrasonidos, ofreciendo al usuario información relativa a número de autobús, ruta etc La tercera clase de sistemas electrónicos de navegación son los 'dispositivos de navegación'. Estos elementos se basan en dispositivos GPS, indicando al usuario tanto su locación, como la ruta que debe seguir para llegar a su punto de destino. Tras la primera etapa de elaboración del esta
Dunai ., L. (2010). Design, modeling and analysis of object localization through acoustical signals for cognitive electronic travel aid for blind people [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8441
Palancia

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49

Tran, Tyler J., Jamis M. Bruening, Andrew G. Bunn, Matthew W. Salzer, and Stuart B. Weiss. "Cluster analysis and topoclimate modeling to examine bristlecone pine tree-ring growth signals in the Great Basin, USA." IOP PUBLISHING LTD, 2017. http://hdl.handle.net/10150/622956.

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Tree rings have long been used to make inferences about the environmental factors that influence tree growth. Great Basin bristlecone pine is a long-lived species and valuable dendroclimatic resource, but often with mixed growth signals; in many cases, not all trees at one location are limited by the same environmental variable. Past work has identified an elevational threshold below the upper treeline above which trees are limited by temperature, and below which trees tend to be moisture limited. This study identifies a similar threshold in terms of temperature instead of elevation through fine-scale topoclimatic modeling, which uses a suite of topographic and temperature-sensor data to predict temperatures across landscapes. We sampled trees near the upper limit of growth at four high-elevation locations in the Great Basin region, USA, and used cluster analysis to find dual-signal patterns in radial growth. We observed dual-signal patterns in ring widths at two of those sites, with the signals mimicking temperature and precipitation patterns. Trees in temperature-sensitive clusters grew in colder areas, while moisture-sensitive cluster trees grew in warmer areas. We found thresholds between temperature- and moisture-sensitivity ranging from 7.4 degrees C to 8 degrees C growing season mean temperature. Our findings allow for a better physiological understanding of bristlecone pine growth, and seek to improve the accuracy of climate reconstructions.

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50

Fonley, Morgan Rae. "Effects of oscillatory forcing on hydrologic systems under extreme conditions: a mathematical modeling approach." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/2075.

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At the large watershed scale, we emphasize the effects of flow through a river network on streamflow under dry conditions. An immediate consequence of assuming dry conditions is that evapotranspiration causes flow in the river network to exhibit oscillations. When all links in the river network combine their flow patterns, the oscillations interact in ways that change the timing and amplitude of the streamflow waves at the watershed outlet. The geometric shape of the river network is particularly important, so we develop an analytic solution for streamflow which emphasizes that importance. Doing hydrology backward is a strategy recently developed by several researchers to deal with uncertainty in measurements of forcing terms applied to hydrologic models. The strategy has also been applied to resolve the assumption of homogeneity on realistic catchments that exhibit many heterogeneous properties. In this work, we demonstrate hydrology in the backward direction applied to two examples: using streamflow at the catchment scale to determine runoff at the hillslope scale and using the hillslope runoff to infer the applied evapotranspiration forcing under the assumption of dry conditions. In order to work across scales, we utilize the analytic solution for streamflow at the outlet of a river network. At the hillslope scale, we develop a soil model to create fluxes consistent with observed soil processes.

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Дисертації з теми "Modeling of hydroacoustic signals"

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