No description available.

This project describes design of the system for diagnosis Parkinson’s disease based on speech. Parkinson’s disease is a neurodegenerative disorder of the central nervous system. One of the symptoms of this disease is disability of motor aspects of speech, called hypokinetic dysarthria. Design of the system in this work is based on the best known segmental features such as coefficients LPC, PLP, MFCC, LPCC but also less known such as CMS, ACW and MSC. From speech records of patients affected by Parkinson’s disease and also healthy controls are calculated these coefficients, further is performed a selection process and subsequent classification. The best result, which was obtained in this project reached classification accuracy 77,19%, sensitivity 74,69% and specificity 78,95%.

No description available.

The aim of this project has been to investigate whether there are correlations to be found between gut microbes and serum metabolites, which could be involved in the development of epilepsy. To do so, metabolomics data containing metabolites and metagenomics data containing bacteria have been integrated and used in a pipeline utilizing the software package DIABLO in R Studio. DIABLO stands for Data Integration Analysis for Biomarker discovery using Latent cOmponents and utilizes multi-block pls-da to integrate multiple omics data sets to find potential biomarkers. The results in this project are mainly divided into two groups, the first group being from taking samples at an early time point, where subjects have not yet developed symptoms of epilepsy and the second group being from taking samples at a late time point, where the subjects have developed epilepsy. To find biomarkers in the data used for the integration, two subgroups are of highest interest, namely subgroup PTE, which is the group that develops epilepsy symptoms after an induced trauma to the brain, as well as subgroup TBI which do not develop epilepsy symptoms after an induced trauma to the brain. Results from the early time point suggests that bacteria such as those from Phelethenecus, Christenselellales, Ventrimonas, Ruminococcaceae and Acetatifactor, as well as metabolites such as LPC 17:0, Indole and Indole-3-carboxyaldehyde might be of interest in finding biomarkers previous to the development of epilepsy after induced brain trauma.  Results from the late time point suggests that bacteria such as those from Muribaculaceae and Avidehalobacter, as well as metabolites such as Dioctyl sulfosuccinate, Canrenone, LPC 18:0, Uric acid, Arjunolic acid and Pseudouridine might be of interest in finding underlying mechanisms behind the existing condition of epilepsy. The hope is that findings in this paper might aid in future development of knowledge behind this disease as well as its underlying mechanisms.

Modern methods of speech synthesis are not suitable for restoration of song signals due to lack of vitality and intonation in the resulted sounds. The aim of presented work is to synthesize melismas met in Lithuanian folk songs, by applying Artificial Neural Networks. An analytical survey of rather a widespread literature is presented. First classification and comprehensive discussion of melismas are given. The theory of dynamic systems which will make the basis for studying melismas is presented and finally the relationship for modeling a melisma with nonlinear and dynamic systems is outlined. Investigation of the most widely used Linear Prediction Coding method and possibilities of its improvement. The modification of original Linear Prediction method based on dynamic LPC frame positioning is proposed. On its basis, the new melisma synthesis technique is presented. Developed flexible generalized melisma model, based on two Artificial Neural Networks – a Multilayer Perceptron and Adaline – as well as on two network training algorithms – Levenberg- Marquardt and the Least Squares error minimization – is presented. Moreover, original mathematical models of Fortis, Gruppett, Mordent and Trill are created, fit for synthesizing melismas, and their minimal sizes are proposed. The last chapter concerns experimental investigation, using over 500 melisma records, and corroborates application of the new mathematical models to melisma synthesis of one performer.

Modern methods of speech synthesis are not suitable for restoration of song signals due to lack of vitality and intonation in the resulted sounds. The aim of presented work is to synthesize melismas met in Lithuanian folk songs, by applying Artificial Neural Networks. An analytical survey of rather a widespread literature is presented. First classification and comprehensive discussion of melismas are given. The theory of dynamic systems which will make the basis for studying melismas is presented and finally the relationship for modeling a melisma with nonlinear and dynamic systems is outlined. Investigation of the most widely used Linear Prediction Coding method and possibilities of its improvement. The modification of original Linear Prediction method based on dynamic LPC frame positioning is proposed. On its basis, the new melisma synthesis technique is presented. Developed flexible generalized melisma model, based on two Artificial Neural Networks – a Multilayer Perceptron and Adaline – as well as on two network training algorithms – Levenberg- Marquardt and the Least Squares error minimization – is presented. Moreover, original mathematical models of Fortis, Gruppett, Mordent and Trill are created, fit for synthesizing melismas, and their minimal sizes are proposed. The last chapter concerns experimental investigation, using over 500 melisma records, and corroborates application of the new mathematical models to melisma synthesis of one performer.

Made available in DSpace on 2016-03-15T19:40:41Z (GMT). No. of bitstreams: 1 Rita dos Santos de Carvalho Picinini.pdf: 1897105 bytes, checksum: 40db215aab8bca0781df1d15de88b3d3 (MD5) Previous issue date: 2009-01-27 / Fundo Mackenzie de Pesquisa / Different mathematical skills have been investigated over time and, with the advance of neuroimaging techniques, such as PET (Positron Emission Tomography) and fMRI (functional Magnetic Resonance), central components of arithmetical processing have been identified in the parietal and the pre-frontal cortices. Besides the advances of the neuroimaging techniques, other techniques such as non-invasive brain modulation have also been studied such as the transcranial magnetic stimulation (TMS) and the transcranial direct current stimulation (TDCS) in the involvement of cognitive functions in the area of calculation. This study aimed at investigating the impact of anodal TDCS applied over the left dorsolateral pre-frontal cortex (LDLPFC), right parietal cortex (RPC), left parietal cortex (LPC) while the subject was performing multiplication operations. Fifteen healthy volunteers, students of psychology, aged between 18 and 30 years old, have held subtests of the WAIS III and the multiplication task. The results showed that the anodal TDCS over the RPC improved the performance of men regarding the number of rightness. The influence of TDCS on volunteers who had worse performance took place not on complex tasks, but simple arithmetical ones. Besides, the influence of TDCS on volunteers who had better performance was in complex tasks, not simple ones. These results show that the effects of the TDCS on a certain function depend on the baseline values of each volunteer. The other stimulation conditions over the LDLPFC and LPC did not show any significant results. The TDCS can bring a beneficial effect in calculation tasks, depending on the intensity, polarity, time and location of stimulation, resulting in the increased or diminished cortex excitability. / Diferentes habilidades matemáticas vêm sendo investigadas ao longo dos tempos e, com o avanço das técnicas de neuroimagem, como PET (Tomografia por emissão de Pósitrons) e fMRI (ressonância magnética funcional) componentes centrais no processamento aritmético vêm sendo identificados em córtex parietal e pré-frontal. Além do avanço das técnicas de neuroimagem, outras técnicas como de modulação cerebral não-invasiva também vêm sendo estudadas, como Estimulação Magnética Transcraniana (EMT) e a Estimulação Transcraniana por Corrente Contínua (ETCC) no envolvimento das funções cognitivas com a área de cálculo. Este estudo teve como objetivo investigar o impacto da ETCC anódica quando aplicada no Córtex Pré-Frontal Dorsolateral (CPFDLE), Córtex Parietal Direito (CPD), Córtex Parietal Esquerdo (CPE) no desempenho em operações de multiplicação. Quinze voluntários saudáveis, estudantes de psicologia, com faixa etária entre 18 e 30 anos, realizaram subtestes do WAIS III e a tarefa de multiplicação. Os resultados desse estudo mostraram que a ETCC anódica aplicada no CPD melhorou o desempenho dos homens em relação ao número de acertos. A influência da ETCC em participantes com pior desempenho em Aritmética se deu em tarefa simples de multiplicação, mas não complexa, ao passo que a influência da ETCC em participantes com melhor desempenho em Aritmética se deu em tarefa complexa de multiplicação, mas não em simples. Tais resultados sinalizam que os efeitos da estimulação em uma determinada função dependem dos valores de linha de base de cada participante As outras condições de estimulações, CPFDLE e CPE não resultaram em efeitos significativos. A ETCC pode produzir um efeito benéfico em tarefas de cálculo, dependendo da intensidade, polaridade, tempo e localização da estimulação, podendo resultar em aumento ou diminuição na excitabilidade do córtex.

La capacité humaine de reconnaitre et d’identifier de nombreux individus uniquement grâce à leur voix est unique et peut s’avérer cruciale pour certaines enquêtes. La méconnaissance de cette capacité jette cependant de l’ombre sur les applications dites « légales » de la phonétique. Le travail de thèse présenté ici a comme objectif principal de mieux définir les différents processus liés au traitement des voix dans le cerveau et les paramètres affectant ce traitement. Dans une première expérience, les potentiels évoqués (PÉs) ont été utilisés pour démontrer que les voix intimement familières sont traitées différemment des voix inconnues, même si ces dernières sont fréquemment répétées. Cette expérience a également permis de mieux définir les notions de reconnaissance et d’identification de la voix et les processus qui leur sont associés (respectivement les composantes P2 et LPC). Aussi, une distinction importante entre la reconnaissance de voix intimement familières (P2) et inconnues, mais répétées (N250) a été observée. En plus d’apporter des clarifications terminologiques plus-que-nécessaires, cette première étude est la première à distinguer clairement la reconnaissance et l’identification de locuteurs en termes de PÉs. Cette contribution est majeure, tout particulièrement en ce qui a trait aux applications légales qu’elle recèle. Une seconde expérience s’est concentrée sur l’effet des modalités d’apprentissage sur l’identification de voix apprises. Plus spécifiquement, les PÉs ont été analysés suite à la présentation de voix apprises à l’aide des modalités auditive, audiovisuelle et audiovisuelle interactive. Si les mêmes composantes (P2 et LPC) ont été observées pour les trois conditions d’apprentissage, l’étendue de ces réponses variait. L’analyse des composantes impliquées a révélé un « effet d’ombrage du visage » (face overshadowing effect, FOE) tel qu’illustré par une réponse atténuée suite à la présentation de voix apprise à l’aide d’information audiovisuelle par rapport celles apprises avec dans la condition audio seulement. La simulation d’interaction à l’apprentissage à quant à elle provoqué une réponse plus importante sur la LPC en comparaison avec la condition audiovisuelle passive. De manière générale, les données rapportées dans les expériences 1 et 2 sont congruentes et indiquent que la P2 et la LPC sont des marqueurs fiables des processus de reconnaissance et d’identification de locuteurs. Les implications fondamentales et en phonétique légale seront discutées. / The human ability to recognize and identify speakers by their voices is unique and can be critical in criminal investigations. However, the lack of knowledge on the working of this capacity overshadows its application in the field of “forensic phonetics”. The main objective of this thesis is to characterize the processing of voices in the human brain and the parameters that influence it. In a first experiment, event related potentials (ERPs) were used to establish that intimately familiar voices are processed differently from unknown voices, even when the latter are repeated. This experiment also served to establish a clear distinction between neural components of speaker recognition and identification supported by corresponding ERP components (respectively the P2 and the LPC). An essential contrast between the processes underlying the recognition of intimately familiar voices (P2) and that of unknown but previously heard voices (N250) was also observed. In addition to clarifying the terminology of voice processing, the first study in this thesis is the first to unambiguously distinguish between speaker recognition and identification in terms of ERPs. This contribution is major, especially when it comes to applications of voice processing in forensic phonetics. A second experiment focused more specifically on the effects of learning modalities on later speaker identification. ERPs to trained voices were analysed along with behavioral responses of speaker identification following a learning phase where participants were trained on voices in three modalities : audio only, audiovisual and audiovisual interactive. Although the ERP responses for the trained voices showed effects on the same components (P2 and LPC) across the three training conditions, the range of these responses varied. The analysis of these components first revealed a face overshadowing effect (FOE) resulting in an impaired encoding of voice information. This well documented effect resulted in a smaller LPC for the audiovisual condition compared to the audio only condition. However, effects of the audiovisual interactive condition appeared to minimize this FOE when compared to the passive audiovisual condition. Overall, the data presented in both experiments is generally congruent and indicate that the P2 and the LPC are reliable electrophysiological markers of speaker recognition and identification. The implications of these findings for current voice processing models and for the field of forensic phonetics are discussed.

Testing, verification and validation of sensors, components and systems is vital in the early-stage development of new cars with computer-in-the-car architecture. This can be done with the help of the existing technique, hardware-in-the-loop (HIL) testing which, in the close loop testing case, consists of four main parts: Real-Time Simulation Platform, Sensor Simulation PC, Interface Unit (IU), and unit under test which is, for instance, a Vehicle Computing Unit (VCU). The purpose of this degree project is to research and develop a proof of concept for in-house development of an image injection solution (IIS) on the IU in the HIL testing environment. A proof of concept could confirm that editing, customizing, and having full control of the IU is a possibility. This project was initiated by Volvo Cars to optimize the use of the HIL testing environment currently available, making the environment more changeable and controllable while the IIS remains a static system. The IU is an MPSoC/FPGA based design that uses primarily Xilinx hardware and software (Vivado/Vitis) to achieve the necessary requirements for image injection in the HIL testing environment. It consists of three stages in series: input, image processing, and output. The whole project was divided in three parts based on the three stages and carried out at Volvo Cars in cooperation by three students, respectively. The author of this thesis was responsible for the output stage, where the main goal was to find a solution for converting, preferably, AXI4 RAW12 image data into data on CSI2 format. This CSI2 data can then be used as input to serializers, which in turn transmit the data via fiber-optic cable on GMSL2 format to the VCU. Associated with the output stage, extensive simulations and hardware tests have been done on a preliminary solution that partially worked on the hardware, producing signals in parts of the design that could be read and analyzed. However, a final definite solution that fully functions on the hardware has not been found, because the work is at the initial phase of an advanced and very complex project. Presented in this thesis is: important theory regarding, for example, protocols CSI2, AXI4, GMSL2, etc., appropriate hardware selection for an IIS in HIL (FPGA, MPSoC, FMC, etc.), simulations of AXI4 and CSI2 signals, comparisons of those simulations with the hardware signals of an implemented design, and more. The outcome was heavily dependent on getting a certain hardware (TEF0010) to transmit the GMSL2 data. Since the wrong card was provided, this was the main problem that hindered the thesis from reaching a fully functioning implementation. However, these results provide a solid foundation for future work related to image injection in a HIL environment.