Dissertations / Theses on the topic 'Continuous wavelet analysis'

The High Resolution Electrocardiogram (HRECG) is used to detect Ventricular Late Potentials (VLPs) in post-myocardial infarction patients. VLPs are low-amplitude, high-frequency signals that are usually found within the terminal part of the QRS complex. The aim of this research was to develop possible alternative methods and improve existing methods of detecting VLP activity. There are two main topics in this work: applications of the Continuous Wavelet Transform (CWT) and the Discrete Wavelet Transform (DWT) to the HRECG. For the CWT application, a Fractionation Factor (FF) method proposed by previous work was further investigated and improved by combining the CWT and DWT for distinction between patients with and those without VLPs. A Differential Fractionation Factor was proposed as an alternative approach to the FF with better results. Observation in the time-scale plot showed a difference in the energy distribution. A 2-dimensional Fractionation Factor was proposed to quantify this difference. A new concept of local intermittency was investigated to exhibit energy nonuniformity and then a Local Intermittency Factor was developed to quantify the degree of nonuniformity. The energy computed with the CWT was also used for patient distinction. Patients with VLPs may be also characterised by a slow rate of energy decay. The CWT can reveal a difference in ECG irregularity between the patients. A new approach of approximate entropy was implemented to quantify this irregularity. For the DWT application, the DWT can reveal irregularity of VLP activity and it was quantified by the approximate entropy to identify patients with VLPs. The wavelet entropy was utilised as an alternative method to the FF. The energy computed with the DWT was used for patient classification. The potentially promising results of both the CWT and DWT applications were obtained from the methods of computing the energy and approximate entropy

Accelerometer data can be analyzed using a variety of methods which are effective in the clinical setting. Time-series analysis is used to analyze spatiotemporal variables in various populations. More recently, investigators have focused on gait complexity and the structure of spatiotemporal variations during walking and running. This study evaluated the use of time-series analyses to determine gait parameters during running. Subjects were college-age female soccer players. Accelerometer data were collected using GPS-embedded trunk-mounted accelerometers. Customized Matlab® programs were developed that included Gaussian continuous wavelet transform (CWT) to determine spatiotemporal characteristics, detrended fluctuation analysis (DFA) to examine gait complexity and autocorrelation analyses (ACF) to assess gait regularity. Reliability was examined using repeated running efforts and intraclass correlation. Proof of concept was determined by examining differences in each variable between various running speeds. Applicability was established by examining gait before and after fatiguing activity. The results showed most variables had excellent reliability. Test-retest R2 values for these variables ranged from 0.8 to 1.0. Low reliability was seen in bilateral comparisons of gait symmetry. Increases in running speed resulted in expected changes in spatiotemporal and acceleration variables. Fatiguing exercise had minimal effects on spatiotemporal variables but resulted in noticeable declines in complexity. This investigation shows that GPS-embedded trunk-mounted accelerometers can be effectively used to assess running gait. CWT and DFA yield reliable measures of spatiotemporal characteristics of gait and gait complexity. The effects of running speed and fatigue on these variables provides proof of concepts and applicability for this analytical approach.
Master of Science
Fitness trackers have become widely accessible and easy to use. So much so that athletic teams have been using them to track activity throughout the season. Researchers are able to manipulate data generated from the fitness monitors to assess many different variables including gait. Monitoring gait may generate important information about the condition of the individual. As a person fatigues, running form is theorized to breakdown, which increases injury risk. Therefore the ability to monitor gait may be advantageous in preventing injury. The purpose of this study is to show that the methods in this study are reproducible, respond reasonably to changes in speed, and to observe the changes of gait in the presence of fatigue or on tired legs. Three analyses are used in this study. The first method called autocorrelation, overlays acceleration signals of consecutive foot strikes, and determines the similarity between them. The second method utilizes a wave transformation technique that is able to determine foot contact times. The final method attempts to determine any pattern in the running stride. This method looks for changes in the structure of the pattern. Less structure would indicate a stride that is fatigued. The results showed that the methods of gait analysis used in this study were reproducible and responded appropriately with changes in speed. Small changes in gait were observed due to the presence of fatigue. Further investigation into the use of these methods to determine changes in gait due to the presence of fatigue are warranted.

This thesis deals with analysis of dynamic forces and vibrations created during cutting with saw. The analysis is done on both simulation and experimental level. Acquired signals are evaluated with signal tools as STFT, CWT and DWT.

Ce travail consiste à caractériser la variabilité temporelle et spatiale des séries chronologiques de paramètres hydroclimatiques (pluies, débits) au niveau de trois grand bassins au Maroc ; (bassins de la Moulouya, du Sebou et du Tensift) et à chercher les liens entre cette variabilité hydrologique et les fluctuations climatiques matérialisées par différents indices climatiques, NAO, SOI, WMOI. L’approche d’étude est basée le traitement statistique des séries temporelles, liée aux dimensions temps et espace.Les grands bassins versants d'échelle continentale comme le Tensift, le Sebou et la Moulouya en climat méditerranéen sous influence océanique, intègrent sur des grandes surfaces la réponse hydrologique aux changements climatiques et environnementaux (fluctuations du climat, précipitations, débits) à de larges échelles spatiales et temporelles, mais également les modifications du milieu physique d’origine anthropique (changements d’occupation des sols, aménagements…), ce qui rend parfois difficile l’identification des liens entre la variabilité hydrologique et la variabilité climatique. Les principaux objectifs de ce travail sont de déterminer et de quantifier les relations entre la variabilité hydroclimatique et les fluctuations du climat à l’échelle de chaque bassin étudié et de ses principaux sous-bassins, via l'utilisation de méthodes d’analyses spectrales adaptées à l’étude des processus non stationnaires (analyse en ondelettes continues, analyse de la cohérence par ondelettes). Plusieurs modes de variabilités sont identifiés à partir de l’analyse par station (pluies et débits), du cycle annuel au mode 16-22 ans, cette analyse sera complétée par une analyse par maille, dont les données sont issues d’un fichier (SIEREM) couvrant la période 1940-1999, où on identifie des fréquences de 1an au 8-16 ans, distinguées sur des périodes différentes au niveau de chaque bassin, permettant ainsi une décomposition de la variabilité spatiale des signaux mis en évidence. Trois principales discontinuités sont identifiées en 1970, 1980 et 2000. La contribution des indices climatiques est assez importante elle est entre 55% et 80%
This work is to characterize the temporal and spatial variability of hydroclimatic time series (rainfall, flow) at three large basins in Morocco; (basins of the Sebou and Moulouya Tensift) and look links between the hydrologic variability and climate fluctuation materialized by various climate indices, NAO, SOI, WMOI. The approach to study is based on statistical analysis of time series, related to time and space dimensions.The great watershed of continental scale as Tensift, Sebou and Moulouya in Mediterranean climate under oceanic influence, integrate over large areas the hydrological response to climate and environmental changes (climate fluctuations, precipitation, flows) not only to large spatial and temporal scales, but also to changes in the physical environment anthropogenic (land use changes, developments ...), which sometimes makes difficult to identify the links between hydrological variability and climate variability. The main objective of this work is to determine and quantify the relationships between hydrological variability and climate fluctuations (regionalised precipitation, climate change indexes) across each studied basin and its main sub-basins, via using spectral analysis methods adapted to the study of non-stationary processes (continuous wavelet analysis, coherence analysis wavelet). Many modes of variability are identified from the station analysis (rainfall and flow rates), the annual cycle to 16-22 years, this analysis will be complemented by a grid analysis, the data come from a (SIEREM) file covering the period from 1940 to 1999, which will allow a better understanding of the spatial variability of signals set highlighted. Which is identified frequencies the 1 year 8-16 years, distinguished different time periods at each basin, three main discontinuities identified in 1970, 1980 and 2000. The contribution of climatic indices is important enough it is between 55% and 80%

Dissertação (mestrado) - Pontifícia Universidade Católica do Paraná, Curitiba, 2009
Bibliografia: f. 47-52
Uma das questões mais importantes na interface de computador-cérebro é a análise de padrões cerebrais, geralmente representados por sinais elétricos. Este trabalho tem como objetivo introduzir uma ferramenta de mineração de dados para sinais de EEG, anali
One of the most important issues in brain-computer interface (BCI) is the analysis of patterns in brain states generally represented by electrical signals. The main objective of this work is to present an exploratory approach on electroencephalographic (E

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Na manutenção preditiva, as análises dos sinais de vibração e das partículas do óleo são frequentemente utilizadas para o diagnóstico de falhas em redutores, porque elas contêm informações das condições de seus elementos mecânicos. Os sinais de vibração de um redutor geralmente têm muito ruído e a relação sinal-ruído é tão baixa que a extração de informações dos componentes do sinal é muito difícil, especialmente em situações práticas. Uma das soluções para este problema é a aplicação de técnicas de processamento do sinal para facilitar a obtenção de informações. Neste trabalho, uma técnica de cancelamento de ruído, a média temporal síncrona (TSA), e outra técnica da transformada contínua de wavelet de Morlet foram desenvolvidas para extração de recursos e diagnóstico de diferentes tipos de danos locais da engrenagem. Estas técnicas são aplicadas em sinais medidos em uma bancada experimental, que consiste em um par engrenado acoplado a um motor e a um gerador. Outro método para monitorar o estado do sistema é pela análise de partículas presente no óleo provenientes do desgaste das engrenagens. Avaliando a quantidade, formato, tamanho e material das partículas é possível obter informações das condições do equipamento e do tipo de desgaste ocorrido. Neste trabalho, foram feitas a análise do óleo pelas técnicas da ferrografia e contagem de partículas. A parte experimental deste trabalho foi dividida em dois experimentos. No primeiro experimento as condições de um par engrenado durante toda a sua vida útil foi monitorada, enquanto que no segundo experimento, um entalhe foi feito na raiz do dente simulando uma trinca por fadiga. A análise das partículas de óleo mostrou quais tipos de desgastes estava ocorrendo e a técnica da transformada contínua de wavelet mostrou-se precisa na identificação de falhas em dentes de engrenagens, sendo possível indicar em qual dente a falha estava se desenvolvendo.
At the predictive maintenance, the vibration signals analysis and oil particles analysis are frequently used to diagnose failures in a gearbox, because they contain information about the condition of its mechanic’s elements. The vibration signals of a gearbox usually have a lot of noise and the ratio ‘signal-noise’ is very low, making the extraction of information from the signals component very hard, especially in a practical situation. One of the solutions to this problem is the application of technics of signal processing, to improve the collection of information. At this study, a technique of noise cancellation, Temporal Synchronous Average (TSA) and another technique called continuous transform with the Morlet wavelet were executed for the extraction of resources and diagnostics of different type of gears local damages. Those methods are applied to signals measured on an experimental test stand, consisting of a gearbox with an engine and a generator. Another method for monitoring system wear is by analyzing wear particles in the oil generated due to the wear on the gears. By evaluating the quantity, shape, size and material of the particles it is possible to obtain information about the conditions of the equipment and the type of wear that has occurred. During this work, it was done the analysis of the oil by the techniques of ferrography and particle counting. The experimental part of this study was divided into two experiments. On the first experiment was monitored the conditions of a couple meshed throughout its useful life and in the second was made a notch in the root of the tooth simulating a crack by fatigue. The analysis of the oil particles showed what types of wear was occurring and the technique of the continuous wavelet transform was accurate in the identification of defects in gear's teeth, and it was possible to indicate which tooth was failing.

Today, UltraSound (US) images are often used in medical examination and surgery. An improvement of the quality of these US-images will lead to many advantages, which is a big motivation for research on this field. One obstacle in improving the quality of the images is the presence of noise and texture. In order to distinguish this unwanted information from the interesting objects, different techniques can be used. Characteristic features, such as the ability to find vague contours, small objects or edges of small strength, decides if the technique is suitable for analysing noisy signals. This thesis presents different techniques for finding objects in US-images by using the continuous wavelet-transform. One observation from the analysis is that for edge-detectors using the wavelet-transform at a single scale, there is a compromise between accuracy and reliability. One has to choose between detecting small objects or vague contours. At fine scales one is able to detect small objects, but not objects with a vague contour without including redundant information. At coarse scales one is able to detect vague contours without including redundant information, but one will not detect small objects. The Lipschitz-regularity and the length of a maxima-line in the time-scale plane works well to find the points where the signal changes with a long duration, but is less suitable to find small objects and to remove unwanted information. By using the value of the wavelet-transform at several scales, it is possible to find vague contours in images, small objects, and edges of small strength compared to the strength of the noise. Another important observation from the analysis is that use of the circumference of objects is appropriate in order to find the most important objects in an image. Using this information has been very useful with respect to the analysis of US-images. Medical ultra-sound images are in general of varying quality. In addition the quality of a US-image will typically change within the signal, and changes with respect to the quality of the contour of objects and the influence of noise. The technique which in general is most reliable and produces the best representations of the US-images analysed in this thesis, uses information about the amplitude of the wavelet-transform both within and across scales, in addition to information about the circumference of the objects. This combined edge-detector is reliable with respect to represent the important objects in the image, and this representation is often easily obtained by the edge-detector.

L'objectif de ce travail a été de développer une méthodologie de télédétection novatrice, s'appuyant sur des plateformes existantes, de suivi des principaux facteurs influençant la dynamique côtière. Lors de mon étude j'ai développé des suivis basés sur un outil classique: l'altimétrie satellitaire. Mon approche s'est appuié sur les nouvelles missions spatiales dont j'ai évalué l'apport sur la zone côtière qui est la plus critique qui est la plus critique du point de vue socio-économique. J'ai plus spécifiquement regardé la façade atlantique entre La Rochelle et Bayonne. Je me suis ensuite intéressée à une technique originale basée sur la réflexion des ondes GNSS (GNSS-R). Ces outils nous permettent de surveiller précisément les diverses ondes de marée et de détecter des phénomènes plus singuliers comme la tempête Xynthia (2010) qui a affectée le Sud de l'Europe. Ces outils démontrent qu'il est possible aussi de suivre la dynamique côtière liée aux variations de houle et son impact sur l'érosion côtière, et même les effets de la forte dépression atmosphérique associée à Xynthia et qui a eu un impact visible sur le niveau local de l'océan atlantique. Ma thèse repose sur deux approches complémentaires basées sur deux échelles d'analyse, l'une globale associée à l'altimétrie satellitaire l'autre plus locale, dédiée à la détection des évènements extrêmes et basée sur le réflectométrie. La première étude s'appuie sur différentes missions altimétriques et nous a permis de suivre les variations du niveau de la mer de la côte atlantique française au Sud du golfe de Gascogne durant la période de 1995-2015. Les données SARAL, dont l'empreinte au sol au de l'ordre de 6 km, montrent qu'il est maintenant possible de s'approcher de la bande côtière jusqu'à ~10km avec une grande précision (~20cm). La seconde application repose sur le GNSS-R que nous avons utilisé pour suivre la partie protégée de la baie de Saint Jean de Luz. Là encore les résultats sont exceptionnels puisqu'ils nous ont permis de suivre l'impact de la tempête Xynthia. J'ai ainsi mis en évidence qu'il était possible avec un seul instrument de suivre les effets des marées, et les effets des surcotes marines qui, associées à l'impact de la pression atmosphérique, donnent une bonne corrélation (R=0.8 entre la composante RC3 et les surcôtes, et R=0.72 avec la pression atmosphérique) durant la tempête. Enfin nous avons aussi regardé ce qui se passe lors de la transition eaux continentales/océaniques pour les deltas du Fleuve Rouge et du Mékong (Vietnam). Et mêmes si les séries temporelles sont assez courtes, les résultats sont plus qu'encourageant puisqu'ils nous ont permis de de suivre les épisodes de crues associées à deux tempêtes tropicales (Mirinae et Nida) et de mesurer le retard entre les chutes de pluies et la propagation de l'onde crue qui montre dans le cas présent un délai de de 48h pour Nida. Grâce au déploiement dans de nombreux pays de réseaux GNSS permanents, cette technique peut être appliquée lorsqu'une station GNSS permanente est située près du rivage. L'approche GNSS-R peut être alors utilisée pour le suivi des variations du niveau de la mer mais aussi l'impact d'évènements extrêmes. Pour cela nous avons utilisé 3 mois d'enregistrements (janv-mars 2010) de la station GNSS de Socoa, pour déterminer les composantes, court terme, de la marée dans les signaux GNSS-R et pour identifier la tempête Xynthia
The objective of this PhD thesis was to develop an innovative remote sensing methodology, based on existing platforms, to monitor the main factors influencing coastal dynamics. We propose monitoring based on a classic tool i.e. satellite altimetry but with a focus on new space missions (SARAL, Sentinel-3). Whose contributions will be evaluated, particularly in the coastal zone, which is the most critical from a socio-economic point of view? I have focused my attention on the French Atlantic coast between La Rochelle and Bayonne. We will also rely on an original technique based on the reflection of GNSS positioning satellites (technical known as GNSS-R). These tools will allow us to precisely monitor the various tidal waves, but they have also allowed us to detect more unusual phenomena such as the extreme event of 2010: the storm Xynthia that affected the coasts of southern Europe. These tools demonstrate that it is also possible will also be able to see to monitor the coastal dynamics related to swell variations and its impact on coastal erosion, and even the effects of the strong atmospheric depression associated with Xynthia, which has had a measurable impact on the local sea level of the Atlantic Ocean. My thesis is focused on two complementary approaches based on two scales of study: the first one is global and used satellite altimetry, the second one is more local and focused on the extreme event detection and it is based on the GNSS reflectometry. The first study, which I carried out, relies on different satellite altimetry missions (ERS-2, Jason- 1/2/3, ENVISAT, SARAL) which allowed us to follow the sea level variations (SSH) from the French Atlantic coast to the south of the Bay of Biscay during the 1995-2015 period. SARAL data, including a footprint of around 6 km, show that it is now possible to approach the coastal fringe up to ~ 10 km with a great precision (RMSE ~ 20 cm). The second application is based on the GNSS-R methodology that we used to track SSH in the inner part of the bay of Saint Jean de Luz - Socoa during the storm Xynthia. Here again the results are exceptional since they allowed us to follow the impact of the storm Xynthia on the local level of the ocean. I thus highlighted that it was possible with only one instrument to follow the effects of the tides, and even the effects of the marine surges which associated to the impact of the atmospheric pressure on the sea level give a good correlation (R = 0.77 between the RC3 component and the surge, and R = 0.73 with the atmospheric pressure) during storm. Finally we also looked at what is happening in the transition between continental and oceanic waters for the deltas of the Red River and Mekong in Vietnam. And, even if the time series are rather short or truncated (Red River) the results are more than encouraging since they allowed us to follow the flooding events associated with two tropical storms (Mirinae and Nida) and to measure the delay between the rain falls and the propagation of the flood wave which shows in this case a delay of 48 h for Nida. With the deployment of permanent GNSS networks in many countries, this technique can be applied when a permanent GNSS station is located near the shore. The GNSS-R approach can be used to monitor sea level variations but also the effect of extreme events. For that we used 3 months of recordings (January-March 2010) from the Socoa GNSS station to determine the tidal components in the GNSS-R signals and to identify the Xynthia storm

This thesis deals with time-frequency analysis of electrograms measured on isolated guinea pig hearts perfused according to Langendorff. Time-frequency analysis is based on algorithms Matching Pursuit and Wigner-Ville Distribution. The theoretical part describes the basics of electrocardiography, measurement on isolated hearts, the theory of approximation method Matching Pursuit and its combination with the Wigner-Ville distribution spectrum showing the energy density of the signal. Also other common approaches of time-frequency analysis are presented including the theory of continuous wavelet transform. The presented algorithms were tested on a set of electrograms, on which were induced ischemia within measurement followed by reperfusion. The proposed method allows for the fast detection of ischemia without any a priori knowledge of the signal, and also serves as a tool for measurement of EG important points and intervals. In the conclusion efficacy of the method was presented and its possible uses has been discussed.

De nos jours, les mesures sans contact du signal cardiaque du patient en utilisant le radar Doppler a suscité un intérêt considérable chez les chercheurs, surtout que les électrocardiographes traditionnels avec des électrodes fixes ne sont pas pratiques dans certains cas comme les nourrissons ou les victimes de brûlure. En raison de la sensibilité des micro­ondes à de petits mouvements, le radar a été utilisé comme système de surveillance de l'activité cardio-pulmonaire humaine. Selon l'effet Doppler, un signal de fréquence constante est transmis vers la cible ayant un déplacement variable puis réfléchi. Le signal réfléchit possède une variation de phase par rapport au temps. Dans notre cas, la cible est la poitrine du patient; Le signal réfléchi de la poitrine de la personne contient le signal cardiorespiratoire. Le système est basé sur un analyseur de réseau vectoriel et deux antennes cornet. Le S21 est calculé en utilisant un analyseur de réseau. La variation de phase de S21 contient des informations de l'activité cardio-pulmonaire. Des techniques de traitement sont utilisées pour extraire le signal cardiaque de la variation de la phase de S21 . Cette thèse présente une étude comparative dans la détection des signaux de battements cardiaques au niveau de la puissance rayonnée et de la fréquence opérationnelle. Les puissances rayonnées sont comprises entre 3 et -17 dBm et les fréquences opérationnelles utilisées sont 2.4, 5.8, 1 0 et 20 GHz. Cela permet de spécifier la fréquence opérationnelle optimale, qui donne un compromis entre la puissance minimale émise ainsi que la complexité du système de mesure. De plus, une étude comparative entre plusieurs méthodes de traitement de signal est proposée pour extraire la meilleure méthode qui permet de mesurer le signal cardiaque et par suite extraire ses paramètres. Des techniques de traitement basées sur des transformées en ondelettes ou le filtrage classique sont présentées et utilisées afin de faire une comparaison entre elles. Le paramètre extrait dans cette thèse est le taux des battements cardiaques. Les mesures ont été effectuées simultanément avec un électrocardiographe afin de valider les mesures du signal cardiaque. Puisque la personne peut se déplacer d'une pièce à une autre à l'intérieur de son domicile, des mesures des quatre côtés de la personne et derrière un mur sont réalisées. Ajoutons une approche de modélisation fondée sur la mesure cardio-respiratoire pour une personne qui exerce une marche en avant. De plus, une comparaison entre un système à micro-ondes à simple et deux antennes pour une personne qui prend son souffle est effectuée afin de tester la précision du système à antenne unique par rapport au a la deuxième. Par suite, des mesures sont effectuées pour une personne qui respire en utilisant un système à une seule antenne
Nowadays, contact-less monitoring patient's heartbeat using Doppler radar has attracted considerable interest of researchers, especially when the traditional electrocardiogram (ECG) measurements with fixed electrodes is not practical in some cases like infants at risk or sudden infant syndrome or burn victims. Due to the microwave sensitivity toward tiny movements, radar has been employed as a noninvasive monitoring system of human cardiopulmonary activity. According to Doppler effect, a constant frequency signal reflected off an object having a varying displacement will result in a reflected signal, but with a time varying phase. In our case, the object is the patient's chest; the reflected signal of the person's chest contains information about the heartbeat and respiration. The system is based on a vector network analyzer and 2 horn antennas. The S21 is computed using a vector network analyzer. The phase variation of S21 contains information about cardiopulmonary activity. Processing techniques are used to extract the heartbeat signal from the S21 phase. This thesis presents a comparative study in heartbeat detection, considering different radiated powers and frequencies. The radiated powers used are between 3 and -17 dBm and the operational frequencies used are 2.4, 5.8, 10 and 20 GHz. This helps to make a compromise between the minimum power emitted and the complexity of the measurement system. In addition, a comparative study of several signal processing methods is proposed to extract the best technique for heartbeat measurement and thus to extract its parameters. Processing techniques are based on wavelet transforms and conventional filtering in order to make a comparison between them. The parameter extracted in this thesis is the heartbeat rate HR. Measurements were performed simultaneously with a PC-based electrocardiograph to validate the heartbeat rate measurement. Since the person can move from a room to another inside his home, measurements from the four sides of the person and behind a wall are performed. In addition, a modeling approach based on cardio-respiratory measurement for a person who is walking forward is presented. Furthermore, a comparison between single and two-antenna microwave systems for a non-breathing person is carried out to test the accuracy of the single-antenna system relative to the two ­antenna microwave system. After that, measurements are performed using one antenna microwave system for a person who breathes normally

No
Word spotting in continuous speech is considered a challenging issue due to dynamic nature of speech. Literature contains a variety of novel techniques for the isolated word recognition and spotting. Most of these techniques are based on pattern recognition and similarity measures. This paper amalgamates the use of different techniques that includes wavelet transform, feature extraction and Euclidean distance. Based on the acoustic features, the proposed system is capable of identifying and localizing a target (test) word in a continuous speech of any length. Wavelet transform is used for the time-frequency representation and filtration of speech signal. Only high intensity frequency components are passed to feature extraction and matching process resulting robust performance in terms of matching as well as computational cost.

碩士
國立成功大學
電機工程學系碩博士班
101
An automatic computer-aided digital signal processor for Doppler radar vital signs healthcare monitoring is described. A voltage waveform signal containing heart-beat and respiration signatures are band-pass filtered for the heart signal. The proposed method, combination of continuous wavelet transform (CWT) and peak finding, is used to adaptively detect heart-beat rate for each of these phase modulated Doppler radar signals. Involving in electrocardiogram, well-developed medical aid, realizes a complete non-contact vital signs monitoring system. An investigation of heart rate variability (HRV) measurements is adopted for the cardiopulmonary disease prognosis. Experimental results show that the proposed algorithm not only provides an accurate heart rate detection but also be able to evaluate heart rate variability (HRV).

碩士
建國科技大學
機電光系統研究所
94
The purpose of this research is to study the effect for acupuncture by analysis of EEG using technology of continuous wavelet transform (CWT). According to definition of EEG, the signal was decomposed into a combination of delta(0.5-4Hz), theta(5-8Hz), alpha(8-13Hz), and beta(13-30Hz). There are 24 male normal subjects were enrolled in this study, and their age is between 20 and 24 years old. Electrode points measured are at the position of T3 and T4 in temporal areas. All the measuring time is 30 minutes. Subjects are eyes-closed in 5 minutes before acupuncture. Then, the EEG signal is continuously recorded for 20 minutes. During this time, subjects keep eyes-closed and conscious. The EEG keeps on recording for 5 minutes after acupuncture. A continuous wavelet transform maps a time function into a two-dimensional function of time and frequency. For characteristic and quantitative analysis, we proposed a analytic time-frequency method to obtain energy from each wave band of EEG, and a Wilcoxon signed rank test is employed to see if acupuncture is significant. The results show that: 1. After deep acupuncture, left beta wave decline (12~16%) statistically between 9th and 20th minutes, and so does right beta wave between 10th and 20th minutes (10~14%). 2. After minimal acupuncture, left alpha wave decline (6-13%) statistically between 7th and 14th minutes, and so does right alpha wave (12-18%). Left beta wave decline (17~20%) statistically between 10th and 20th minutes, and so does right beta wave between 9th and 18th minutes (13~21%). 3. There is no significant variation between 21th~25th minutes the time no acupuncture.

碩士
南開科技大學
福祉科技與服務管理所
102
ABSTRACT （1）Objectives： The purposes of this study are twofold: first, we explore the dynamic relationship between female wage and fertility, and second, we investigate the dynamic relationship between fertility and ageing population in Taiwan. The results generated from this study not only can provide a complete picture of the dynamic relationship between business cycle and low fertility together with ageing population but also can serve as an important reference for the policymaker to increase the female fertility in Taiwan . （2）Materials and Methods The monthly data used in this study include crude birth rate, marriage rate, number of aged 65 and over and female wage from 1978-2012 in five municipal areas in Taiwan. We performed the continuous wavelet analysis to investigate the eight hypotheses:（H1）female wage positively leading fertility,（H2）female wage negatively leading fertility,（H3）female wage positively lagging fertility,（H4）female wage negatively lagging fertility,（H5）fertility positively leading ageing population,（H6）fertility negatively leading ageing population,（H7）fertility positively lagging ageing population, and（H8）fertility negatively lagging ageing population hypotheses. The first four hypotheses are corresponding to the first purpose of this study, and the rest four hypotheses are corresponding to the second purpose of this study. （3）Results Our results showed that there exists a negative relationship between female wage and fertility, and in the short and long run, fertility is a leading index of the female wage, a result consistent with the female wage negatively lagging fertility hypothesis. In the medium run, however, fertility positively led the female wage, a result predicted by the female wage positively lagging hypothesis. In addition, there exists a negative relationship between fertility and ageing population, and the fertility negatively leading ageing population hypothesis is validated for all periodicities in Taiwan area. （4）Conclusion The results of this study found that the way to increase fertility is the most important policy to reduce the ageing population. In addition, the female wage negatively (positively) lagging fertility hypothesis was observed in the short and long (medium) run. Therefore, an appropriate fertility plan may serves as an important policy instrument to stimulate economics in the short and long run. In the medium run, Taiwan government should encourage fertility that increases labor force benefit the economic development for Taiwan. Keywords : Continuous wavelet analysis, Ageing population, Lowest-low Fertility, Business cycle.

Dissertação de Mestrado em Métodos Quantitativos em Finanças, apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra
O presente trabalho pretende aprofundar o estudo de três índices bolsistas europeus, PSI 20, IBEX 35 e DAX 30, utilizando para tal uma abordagem recente, a análise com onduletas. As características únicas das séries financeiras, como a ausência de estacionaridade, a presença de clusters de volatilidade e do efeito alavanca, exigem uma ferramenta minuciosa capaz de analisar profundamente a série. A análise com onduletas permite realizar a decomposição da série em vários níveis de detalhe e de aproximação, tendo em conta o domínio do tempo e o domínio da frequência simultaneamente. A relação entre os rendimentos dos três índices em estudo é avaliada utilizando uma abordagem baseada em onduletas. Os resultados mostram como estes três índices bolsistas se encontram bem correlacionados para frequências baixas.
The aim of this work is to develop the study of three european stock market indexes, PSI 20, IBEX 35 and DAX 30, using a very promising tool called: wavelet analysis. Time series of financial asset returns often exhibit volatility clustering and leverage effects which require a great tool, capable of decomposing a time series in a set of detail and approximation coeficients into a time-frequency space. The link between the three stock market indexes is analised with a wavelet-based measure. The results show that these european stock markets are highly integrated at lower frequencies.

The idea of time series forecasting techniques is that the past has certain information about future. So, the question of how the information is encoded in the past can be interpreted and later used to extrapolate events of future constitute the crux of time series analysis and forecasting. Several methods such as qualitative techniques (e.g., Delphi method), causal techniques (e.g., least squares regression), quantitative techniques (e.g., smoothing method, time series models) have been developed in the past in which the concept lies in establishing a model either theoretically or mathematically from past observations and estimate future from it. Of all the models, time series methods such as autoregressive moving average (ARMA) process have gained popularity because of their simplicity in implementation and accuracy in obtaining forecasts. But, these models were formulated based on certain properties that a time series is assumed to possess. Classical decomposition techniques were developed to supplement the requirements of time series models. These methods try to define a time series in terms of simple patterns called trend, cyclical and seasonal patterns along with noise. So, the idea of decomposing a time series into component patterns, later modeling each component using forecasting processes and finally combining the component forecasts to obtain actual time series predictions yielded superior performance over standard forecasting techniques. All these methods involve basic principle of moving average computation. But, the developed classical decomposition methods are disadvantageous in terms of containing fixed number of components for any time series, data independent decompositions. During moving average computation, edges of time series might not get modeled properly which affects long range forecasting. So, these issues are to be addressed by more efficient and advanced decomposition techniques such as Wavelets and Empirical Mode Decomposition (EMD). Wavelets and EMD are some of the most innovative concepts considered in time series analysis and are focused on processing nonlinear and nonstationary time series. Hence, this research has been undertaken to ascertain the predictability of nonstationary time series using wavelet and Empirical Mode Decomposition (EMD) based ARMA models. The development of wavelets has been made based on concepts of Fourier analysis and Window Fourier Transform. In accordance with this, initially, the necessity of involving the advent of wavelets has been presented. This is followed by the discussion regarding the advantages that are provided by wavelets. Primarily, the wavelets were defined in the sense of continuous time series. Later, in order to match the real world requirements, wavelets analysis has been defined in discrete scenario which is called as Discrete Wavelet Transform (DWT). The current thesis utilized DWT for performing time series decomposition. The detailed discussion regarding the theory behind time series decomposition is presented in the thesis. This is followed by description regarding mathematical viewpoint of time series decomposition using DWT, which involves decomposition algorithm. EMD also comes under same class as wavelets in the consequence of time series decomposition. EMD is developed out of the fact that most of the time series in nature contain multiple frequencies leading to existence of different scales simultaneously. This method, when compared to standard Fourier analysis and wavelet algorithms, has greater scope of adaptation in processing various nonstationary time series. The method involves decomposing any complicated time series into a very small number of finite empirical modes (IMFs-Intrinsic Mode Functions), where each mode contains information of the original time series. The algorithm of time series decomposition using EMD is presented post conceptual elucidation in the current thesis. Later, the proposed time series forecasting algorithm that couples EMD and ARMA model is presented that even considers the number of time steps ahead of which forecasting needs to be performed. In order to test the methodologies of wavelet and EMD based algorithms for prediction of time series with non stationarity, series of streamflow data from USA and rainfall data from India are used in the study. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability by the proposed algorithm is checked in two scenarios, first being six months ahead forecast and the second being twelve months ahead forecast. Normalized Root Mean Square Error (NRMSE) and Nash Sutcliffe Efficiency Index (Ef) are considered to evaluate the performance of the proposed techniques. Based on the performance measures, the results indicate that wavelet based analyses generate good variations in the case of six months ahead forecast maintaining harmony with the observed values at most of the sites. Although the methods are observed to capture the minima of the time series effectively both in the case of six and twelve months ahead predictions, better forecasts are obtained with wavelet based method over EMD based method in the case of twelve months ahead predictions. It is therefore inferred that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place. Finally, the study concludes that the wavelet based time series algorithm could be used to model events such as droughts with reasonable accuracy. Also, some modifications that could be made in the model have been suggested which can extend the scope of applicability to other areas in the field of hydrology.

The current research work aims at the development of Biomedical Neuroengineering tools (Biotechnologies) for the in-depth functional study, rapid diagnosis, continuous monitoring and well-timed management of acute and chronic brain disorders, of individuals that are subjected to or suffer from any kind of systemic hypoxaemia or more localized brain hypoxia; as well as the functional assessment and continuous control of adaptability during the training of “altinauts” and generally of individuals that practice activities and function within environments of increased visual-cognitive-motor response demands (a type of brain “stress test”). For this purpose, we subject the entire visuocognitive system, from the elementary sensory to the most complex cognitive level, to an experimental test of categorical discrimination of complex visuocognitive stimuli, following ultra-rapid visual stimulation that leads to a motor response upon categorization of targets (images of animals elicit productive responses) and to its suppression upon categorization of nontargets (images of nonanimals elicit inhibitory responses). The oscillatory electro-physiological responses that are concurrently recorded at the occipital-temporal-parietal brain areas are analyzed in the time-domain (<20Hz) and in the joint time-frequency domain broadband (1-60Hz) with the Continuous Wavelet Transform that optimizes the multiresolution analysis of the high frequency (≥20Hz) γ-band oscillatory activity. This visuocognitive categorization test takes place in normoxaemic as well as hypoxaemic conditions (monitored reduction in the blood oxygen saturation from ≥97% to around 80% under conditions of hypobaric hypoxia within a hypobaric chamber), in order to assess electrophysiological markers that can detect and capture in the most sensitive and dynamic way even so transient, short-living and rather mild changes in brain function. The statistical parametric analysis of the time-frequency maps and the generalized, statistically safer, method of analysis of variance have established as the most sensitive and reliable the following markers: the major deflections of the evoked potentials, the phase-coherence factor of the oscillations across single-trials and the elicited energy of the evoked/phase-locked and the induced/total oscillatory activity. These electrophysiological markers in conjunction with psychometric tests allow for the investigation of the stages/levels of the decline as well as of the compensatory reserves in the visual-perceptive and cognitive-mental brain functions in order to determine the functional sensitivity thresholds of different brain functions to hypoxia. They open up the way for the functional characterization, the diagnosis and monitoring of brain insults or other acute and chronic pathological brain conditions.
Η παρούσα ερευνητική εργασία στοχεύει στην ανάπτυξη εργαλείων Βιοϊατρικής Νευρομηχανικής (Βιοτεχνολογίες) για την σε βάθος λειτουργική μελέτη, ταχεία διάγνωση, συνεχή παρακολούθηση και έγκαιρη αντιμετώπιση οξέων και χρόνιων εγκεφαλικών διαταραχών, ατόμων που υπόκεινται σε ή πάσχουν από οιαδήποτε μορφή συστηματικής υποξαιμίας ή πιο εντοπισμένης εγκεφαλικής υποξίας, καθώς και για την λειτουργική αξιολόγηση και το συνεχή έλεγχο της προσαρμοστικότητας κατά την εξάσκηση των «υψιβατών», και γενικότερα ατόμων που ασκούν δραστηριότητες και λειτουργούν μέσα σε περιβάλλοντα αυξημένων οπτικο-γνωστικο-κινητικών απαιτήσεων (ένα είδος «στρες τεστ» για τον εγκέφαλο). Για το σκοπό αυτό υποβάλλουμε ολόκληρο το οπτικογνωστικό σύστημα, από το στοιχειώδες αισθητηριακό έως το πιο πολύπλοκο νοητικό επίπεδο, σε μια πειραματική δοκιμασία κατηγορικής διάκρισης σύνθετων οπτικογνωστικών ερεθισμάτων, μετά από υπερταχεία οπτική διέγερση που οδηγεί στην έκλυση κινητικής απάντησης κατά την κατηγοριοποίηση στόχων (εικόνες «ζώων» εκλύουν παραγωγικές αποκρίσεις) και στην καταστολή της κατά την κατηγοριοποίηση μη-στόχων (εικόνες «μη-ζώων» εκλύουν ανασταλτικές αποκρίσεις). Οι ταλαντωτικές ηλεκτροφυσιολογικές αποκρίσεις που συγχρόνως καταγράφονται στις ινιακές-κροταφικές-βρεγματικές περιοχές του εγκεφάλου αναλύονται στο πεδίο του χρόνου (<20Hz) και στο συζευγμένο χρονοφασματικό πεδίο ευρυζωνικά (1-60Hz) με το συνεχή μετασχηματισμό του κυματίου που βελτιστοποιεί την πολυφασματική ανάλυση της υψίσυχνης (≥20Hz) γ-ταλαντωτικής δραστηριότητας. Αυτή η δοκιμασία οπτικογνωστικής κατηγοριοποίησης λαμβάνει χώρα τόσο σε νορμοξαιμικές όσο και υποξαιμικές συνθήκες (ελεγχόμενη μείωση στον κορεσμό του αίματος σε οξυγόνο από ≥97% γύρω στο 80% για 15 λεπτά κάτω από συνθήκες υποβαρικής υποξίας μέσα σε υποβαρικό θάλαμο), προκειμένου να ελέγξουμε ηλεκτροφυσιολογικούς δείκτες που μπορούν να ανιχνεύσουν και να συλλάβουν με τον πιο ευαίσθητο και δυναμικό τρόπο ακόμη και τόσο βραχύβιες και σχετικά ήπιες μεταβολές της εγκεφαλικής λειτουργίας. Η στατιστική παραμετρική ανάλυση των χρονοφασματικών χαρτών και η γενικευμένη, στατιστικά πιο ασφαλής, μέθοδος ανάλυσης των διακυμάνσεων ανέδειξαν ως πλέον ευαίσθητους και αξιόπιστους τους ακόλουθους δείκτες: τις κύριες αιχμές των προκλητών δυναμικών, τον παράγοντα φασικής συνάφειας των ταλαντώσεων μεταξύ των μοναδιαίων καταγραφών και την εκλυόμενη ενέργεια των προκλητών/φασικά-κλειδωμένων και επαγόμενων/ολικών ταλαντώσεων. Οι ηλεκτροφυσιολογικοί αυτοί δείκτες σε συνδυασμό με ψυχομετρικές δοκιμασίες επιτρέπουν τη διερεύνηση των σταδίων/επιπέδων κάμψης καθώς και των αποθεμάτων αντιρρόπησης των οπτικο-αντιληπτικών και γνωστικών-νοητικών λειτουργιών του εγκεφάλου για τον καθορισμό των λειτουργικών ουδών ευαισθησίας διάφορων εγκεφαλικών λειτουργιών στην υποξία. Ανοίγουν μάλιστα το δρόμο. για το λειτουργικό χαρακτηρισμό, τη διάγνωση και την παρακολούθηση εγκεφαλικών προσβολών ή άλλων οξέων και χρόνιων παθολογικών καταστάσεων του εγκεφάλου.