A research program was undertaken to quantify the effect of flow induced erosion on the stability of natural river banks along the Red River in Manitoba. The Erosion Measurement Device (EMD) was designed and built in the Geotechnical Laboratory of University of Manitoba to approximate the erosion rate profiles of soil samples from nine sites along the RedRiver. Two simulations of a natural flood event and one of the same flood with the operation of the Floodway were then used to determine the difference in the lower toe erosion and the slopes reduction of the global factor of safety. These results indicate that the operation of the Floodway does not have negative impact on the stability of river banks upstream of the Floodway inlet.

The master thesis deals with the issue of gaining the respiratory rate from ECG and PPG signals, which are not only in clinical practice widely used measurable signals. The theoretical part of the work outlines the issue of obtaining a breath curve from these signals. The practical part of the work is focused on the implementation of five selected methods and their final evaluation and comparison.

No description available.

We analyzed long-term temperature trends in Utah using a relatively new signal processing method called Empirical Mode Decomposition (EMD). We evaluated the available weather records in Utah and selected 52 stations, which had records longer than 60 years, for analysis. We analyzed daily temperature data, both minimum and maximums, using the EMD method that decomposes non-stationary data (data with a trend) into periodic components and the underlying trend. Most decomposition algorithms require stationary data (no trend) with constant periods and temperature data do not meet these constraints. In addition to identifying the long-term trend, we also identified other periodic processes in the data. While the immediate goal of this research is to characterize long-term temperature trends and identify periodic processes and anomalies, these techniques can be applied to any time series data to characterize trends and identify anomalies. For example, this approach could be used to evaluate flow data in a river to separate the effects of dams or other regulatory structures from natural flow or to look at other water quality data over time to characterize the underlying trends and identify anomalies, and also identify periodic fluctuations in the data. If these periodic fluctuations can be associated with physical processes, the causes or drivers might be discovered helping to better understand the system. We used EMD to separate and analyze long-term temperature trends. This provides awareness and support to better evaluate the extremities of climate change. Using these methods we will be able to define many new aspects of nonlinear and nonstationary data. This research was successful and identified several areas in which it could be extended including data reconstruction for time periods missing data. This analysis tool can be applied to various other time series records.

Three mathematical models, two of primary alkaline battery cathode discharge, and one of primary alkaline battery discharge, are developed, presented, solved and investigated in this thesis. The primary aim of this work is to improve our understanding of the complex, interrelated and nonlinear processes that occur within primary alkaline batteries during discharge. We use perturbation techniques and Laplace transforms to analyse and simplify an existing model of primary alkaline battery cathode under galvanostatic discharge. The process highlights key phenomena, and removes those phenomena that have very little effect on discharge from the model. We find that electrolyte variation within Electrolytic Manganese Dioxide (EMD) particles is negligible, but proton diffusion within EMD crystals is important. The simplification process results in a significant reduction in the number of model equations, and greatly decreases the computational overhead of the numerical simulation software. In addition, the model results based on this simplified framework compare well with available experimental data. The second model of the primary alkaline battery cathode discharge simulates step potential electrochemical spectroscopy discharges, and is used to improve our understanding of the multi-reaction nature of the reduction of EMD. We find that a single-reaction framework is able to simulate multi-reaction behaviour through the use of a nonlinear ion-ion interaction term. The third model simulates the full primary alkaline battery system, and accounts for the precipitation of zinc oxide within the separator (and other regions), and subsequent internal short circuit through this phase. It was found that an internal short circuit is created at the beginning of discharge, and this self-discharge may be exacerbated by discharging the cell intermittently. We find that using a thicker separator paper is a very effective way of minimising self-discharge behaviour. The equations describing the three models are solved numerically in MATLABR, using three pieces of numerical simulation software. They provide a flexible and powerful set of primary alkaline battery discharge prediction tools, that leverage the simplified model framework, allowing them to be easily run on a desktop PC.

Ο στόχος αυτής της εργασίας ήταν η αναγνώριση έξι βασικών κινήσεων του χεριού με χρήση δύο συστημάτων. Όντας θέμα διεπιστημονικού επιπέδου έγινε μελέτη της ανατομίας των μυών του πήχη, των βιοσημάτων, της μεθόδου της ηλεκτρομυογραφίας (ΗΜΓ) και μεθόδων αναγνώρισης προτύπων. Παράλληλα, το σήμα περιείχε αρκετό θόρυβο και έπρεπε να αναλυθεί, με χρήση του EMD, να εξαχθούν χαρακτηριστικά αλλά και να μειωθεί η διαστασιμότητά τους, με χρήση των RELIEF και PCA, για βελτίωση του ποσοστού επιτυχίας ταξινόμησης. Στο πρώτο μέρος γίνεται χρήση συστήματος ΗΜΓ της Delsys αρχικά σε ένα άτομο και στη συνέχεια σε έξι άτομα με το κατά μέσο όρο επιτυχημένης ταξινόμησης, για τις έξι αυτές κινήσεις, να αγγίζει ποσοστά άνω του 80%. Το δεύτερο μέρος περιλαμβάνει την κατασκευή αυτόνομου συστήματος ΗΜΓ με χρήση του Arduino μικροελεγκτή, αισθητήρων ΗΜΓ και ηλεκτροδίων, τα οποία είναι τοποθετημένα σε ένα ελαστικό γάντι. Τα αποτελέσματα ταξινόμησης σε αυτή την περίπτωση αγγίζουν το 75%. / The aim of this work was to identify six basic movements of the hand using two systems. Being an interdisciplinary topic, there has been conducted studying in the anatomy of forearm muscles, biosignals, the method of electromyography (EMG) and methods of pattern recognition. Moreover, the signal contained enough noise and had to be analyzed, using EMD, to extract features and to reduce its dimensionality, using RELIEF and PCA, to improve the success rate of classification. The first part uses an EMG system of Delsys initially for an individual and then for six people with the average successful classification, for these six movements at rates of over 80%. The second part involves the construction of an autonomous system EMG using an Arduino microcontroller, EMG sensors and electrodes, which are arranged in an elastic glove. Classification results in this case reached 75% of success.

Ce travail a pour but le développement de techniques de séparation des contributions acoustique et aérodynamique de champs de pression pariétale sous des écoulements turbulents. Ces techniques de séparation, dites en nombre d'onde, se basent sur les propriétés spatiales de ces fluctuations. La compréhension des phénomènes de chacun des deux types fluctuations de pression pariétale s'exerçant sur la vitre latérale d'un véhicule vient servir un enjeu industriel de réduction du niveau sonore intérieur du bruit de forme des automobiles. Pour ce faire, des expérimentations en soufflerie anéchoïque sont menées afin d'obtenir des mesures de pression pariétale fluctuante pour divers types d'écoulements. Ainsi un écoulement de couche limite turbulente pleinement développée sur plaque plane, des écoulements décollés en aval de trois géométries de marches montantes, ainsi qu'en aval d'un montant de baie sur véhicule réel sont considérés. Une représentation des spectres en nombre d'onde-fréquence (obtenue par la méthode du corrélogramme spatial) est choisie pour séparer les deux contributions. Cette technique ne s'appliquant que sur des écoulements stationnaires et homogènes, de nouvelles techniques basées sur la Décomposition Modale Empirique (EMD) sont utilisées, d'une part pour améliorer la séparation des deux contributions (grâce à l'EMD spatiale), d'autre part pour obtenir une décomposition multi-échelle au cours du temps (grâce à l'Ensemble-EMD). Grâce à ces méthodes, une observation plus précise des phénomènes pariétaux convectifs et acoustiques est proposée.

L'objectif repose sur le traitement et l'analyse des signaux non-stationnaires, multi-composantes. <br />Pour le traitement (filtrage et débruitage), nous proposons de nouveaux outils fondés sur la Transformation de Huang (ou Décomposition modale empirique : EMD). Partant de l'opérateur de Teager-Kaiser, nous proposons un nouvel opérateur de mesure d'interaction entre deux signaux complexes. Nous établissons les liens théoriques avec les représentations temps-fréquence de la classe de Cohen. Nous montrons que c'est une mesure de similarité et qu'il est adapté à la détection de signaux. <br />Pour l'analyse, nous introduisons une nouvelle méthode temps-fréquence basée sur l'utilisation conjointe de l'EMD et de l'opérateur de Teager-Kaiser : la Transformation de Huang-Teager (THT). Pour illustrer ces concepts, des résultats de filtrage, de débruitage, de détection, d'analyse temps-fréquence de signaux sont présentés. Nous terminons par l'analyse et classification des échos de cibles sonars par THT.

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.

La Décomposition Modale Empirique (EMD), est une méthode de décomposition multi-résolution de signaux en fonctions Modes Intrinsèques (IMF) et cela, de manière auto-adaptative. En la couplant avec la transformée de Hilbert, elle devient une méthode d'analyse Temps-Fréquence , la transformée de Hilbert-Huang, permettant d'étudier bon nombre de classes de signaux. Malgré ces nombreuses applications, l'une des plus importantes limites de l'EMD est son manque de formalisme mathématique. A la place d'une interpolation par splines cubiques utilisée dans l'EMD classique, nous avons estimé l'enveloppe moyenne par une solution d'un système d'EDP. Par une méthode variationnelle, nous avons établi un cadre théorique pour prouver les résultats de convergence, d'existence de modes et la propriété de presque orthogonalité de l'EMD. La comparaison avec des bancs de filtres itératifs et les ondelettes, montre l'aspect multi-résolution de l'EMD. Deux nouvelles applications en traitement du signal et de l'image sont présentées : l'extraction des intermittences et mode mixing et la restauration par shrinkage par EMD. Enfin le modèle peut servir de base pour l'étude de l'unicité de la décomposition.