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The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment using vehicle-to-infrastructure (V2I) communication. However, wireless communication using DSRC has shown to exhibit null points, at repeatable distances. The null points are significant and there was unexpected loss in the wireless signal strength along the pathway of the V2I communication. If the wireless connection is poor or non-existent, the V2I safety application will not obtain sufficient data to perform the operation services. In other words, a poor wireless connection between a vehicle and infrastructure (e.g., RSU) could hamper the performance of a safety application. For example, a designer of a V2I safety application may require a minimum rate of data (or packet count) over 1,000 meters to effectively implement a Reduced Speed/Work Zone Warning (RSZW) application. The RSZW safety application is aimed to alert or warn drivers, in a Cooperative Adaptive Cruise Control (CACC) platoon, who are approaching a work zone. Therefore, the packet counts and/or signal strength threshold criterion must be determined by the developer of the V2I safety application. Thus, we selected an arbitrary criterion to develop an empirical method of ascertaining the null points from a DSRC RSU. The research motivation focuses on developing an empirical method of calculating the null points of a DSRC RSU for V2I communication at a highway on/off-ramp. The intent is to improve safety, mobility, and environmental applications since a map of the null points can be plotted against the distance between the DSRC RSU and a vehicle's onboard unit (OBU). The main research question asks: 'What is a more robust empirical method, compared to the horizontal and vertical laws of reflection formula, in determining the null points from a DSRC RSU on a highway on/off ramp?' The research objectives are as follows: 1. Explain where and why null points occur from a DSRC RSU (Chapter 2) 2. Apply the existing horizontal and vertical polarization model and discuss the limitations of the model in a real-world scenario for a DSRC RSU on a highway on/off ramp (Chapter 3 and Appendix A) 3. Introduce an extended horizontal and vertical polarization null point model using empirical data (Chapter 4) 4. Discuss the conclusion, limitations of work, and future research (Chapter 5). The simplest manner to understand where and why null points occur is depicted as two sinusoidal waves: direct and reflective waves (i.e., also known as a two-ray model). The null points for a DSRC RSU occurs because the direct and reflective waves produce a destructive interference (i.e., decrease in signal strength) when they collide. Moreover, the null points can be located using Pythagorean theorem for the direct and reflective waves. Two existing models were leveraged to analyze null points: 1) signal strength loss (i.e., a free space path loss model, or FSPL, in Appendix A) and 2) the existing horizontal and vertical polarization null points from a DSRC RSU. Using empirical data from two different field tests, the existing horizontal and vertical polarization null point model was shown to contain limitations in short distances from the DSRC RSU. Moreover, the existing horizontal and vertical polarization model for null points was extremely challenging to replicate with over 15 DSRC RSU data sets. After calculating the null point for several DSRC RSU heights, the paper noticed a limitation of the existing horizontal and vertical polarization null point model with over 15 DSRC RSU data sets (i.e., the model does not account for null points along the full length of the FSPL model). An extended horizontal and vertical polarization model is proposed that calculates the null point from a DSRC RSU. There are 18 model comparisons of the packet counts and signal strengths at various thresholds as perspective extended horizontal and vertical polarization models. This paper compares the predictive ability of 18 models and measures the fit. Finally, a predication graph is depicted with the neural network's probability profile for packet counts =1 when greater than or equal to 377. Likewise, a python script is provided of the extended horizontal and vertical polarization model in Appendix C. Consequently, the neural network model was applied to 10 different DSRC RSU data sets at 10 unique locations around a circular test track with packet counts ranging from 0 to 11. Neural network models were generated for 10 DSRC RSUs using three thresholds with an objective to compare the predictive ability of each model and measure the fit. Based on 30 models at 10 unique locations, the highest misclassification was 0.1248, while the lowest misclassification was 0.000. There were six RSUs mounted at 3.048 (or 10 feet) from the ground with a misclassification rate that ranged from 0.1248 to 0.0553. Out of 18 models, seven had a misclassification rate greater than 0.110, while the remaining misclassification rates were less than 0.0993. There were four RSUs mounted at 6.096 meters (or 20 feet) from the ground with a misclassification rate that ranged from 0.919 to 0.000. Out of 12 models, four had a misclassification rate greater than 0.0590, while the remaining misclassification rates were less than 0.0412. Finally, there are two major limitations in the research: 1) the most effective key parameter is packet counts, which often require expensive data acquisition equipment to obtain the information and 2) the categorical type (i.e., decision tree, logistic regression, and neural network) will vary based on the packet counts or signal strength threshold that is dictated by the threshold criterion. There are at least two future research areas that correspond to this body of work: 1) there is a need to leverage the extended horizontal and vertical polarization null point model on multiple DSRC RSUs along a highway on/off ramp, and 2) there is a need to apply and validate different electric and magnetic (or propagation) models. / Ph. D. / The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment using vehicle-to-infrastructure (V2I) communication. However, wireless communication using DSRC has shown to exhibit null points, at repeatable distances. The null points are significant and there was unexpected loss in the wireless signal strength along the pathway of the V2I communication. If the wireless connection is poor or non-existent, the V2I safety application will not obtain sufficient data to perform the operation services. In other words, a poor wireless connection between a vehicle and infrastructure (e.g., RSU) could hamper the performance of a safety application. For example, a designer of a V2I safety application may require a minimum rate of data (or packet count) over 1,000 meters to effectively implement a Reduced Speed/Work Zone Warning (RSZW) application. The RSZW safety application is aimed to alert or warn drivers, in a Cooperative Adaptive Cruise Control (CACC) platoon, who are approaching a work zone. Therefore, the packet counts and/or signal strength threshold criterion must be determined by the developer of the V2I safety application. Thus, we selected an arbitrary criterion to develop an empirical method of ascertaining the null points from a DSRC RSU. The research motivation focuses on developing an empirical method of calculating the null points of a DSRC RSU for V2I communication at a highway on/off-ramp. The intent is to improve safety, mobility, and environmental applications since a map of the null points can be plotted against the distance between the DSRC RSU and a vehicle’s onboard unit (OBU). The main research question asks: “What is a more robust empirical method, compared to the horizontal and vertical laws of reflection formula, in determining the null points from a DSRC RSU on a highway on/off ramp?” The research objectives are as follows: 1. Explain where and why null points occur from a DSRC RSU (Chapter 2) 2. Apply the existing horizontal and vertical polarization model and discuss the limitations of the model in a real-world scenario for a DSRC RSU on a highway on/off ramp (Chapter 3 and Appendix A) 3. Introduce an extended horizontal and vertical polarization null point model using empirical data (Chapter 4) 4. Discuss the conclusion, limitations of work, and future research (Chapter 5). The simplest manner to understand where and why null points occur is depicted as two sinusoidal waves: direct and reflective waves (i.e., also known as a two-ray model). The null points for a DSRC RSU occurs because the direct and reflective waves produce a destructive interference (i.e., decrease in signal strength) when they collide. Moreover, the null points can be located using Pythagorean theorem for the direct and reflective waves. Two existing models were leveraged to analyze null points: 1) signal strength loss (i.e., a free space path loss model, or FSPL, in Appendix A) and 2) the existing horizontal and vertical polarization null points from a DSRC RSU. Using empirical data from two different field tests, the existing horizontal and vertical polarization null point model was shown to contain limitations in short distances from the DSRC RSU. Moreover, the existing horizontal and vertical polarization model for null points was extremely challenging to replicate with over 15 DSRC RSU data sets. After calculating the null point for several DSRC RSU heights, the paper noticed a limitation of the existing horizontal and vertical polarization null point model with over 15 DSRC RSU data sets (i.e., the model does not account for null points along the full length of the FSPL model). An extended horizontal and vertical polarization model is proposed that calculates the null point from a DSRC RSU. There are 18 model comparisons of the packet counts and signal strengths at various thresholds as perspective extended horizontal and vertical polarization models. This paper compares the predictive ability of 18 models and measures the fit. Finally, a predication graph is depicted with the neural network’s probability profile for packet counts =1 when greater than or equal to 377. Likewise, a python script is provided of the extended horizontal and vertical polarization model in Appendix C. Consequently, the neural network model was applied to 10 different DSRC RSU data sets at 10 unique locations around a circular test track with packet counts ranging from 0 to 11. Neural network models were generated for 10 DSRC RSUs using three thresholds with an objective to compare the predictive ability of each model and measure the fit. Based on 30 models at 10 unique locations, the highest misclassification was 0.1248, while the lowest misclassification was 0.000. There were six RSUs mounted at 3.048 (or 10 feet) from the ground with a misclassification rate that ranged from 0.1248 to 0.0553. Out of 18 models, seven had a misclassification rate greater than 0.110, while the remaining misclassification rates were less than 0.0993. There were four RSUs mounted at 6.096 meters (or 20 feet) from the ground with a misclassification rate that ranged from 0.919 to 0.000. Out of 12 models, four had a misclassification rate greater than 0.0590, while the remaining misclassification rates were less than 0.0412. Finally, there are two major limitations in the research: 1) the most effective key parameter is packet counts, which often require expensive data acquisition equipment to obtain the information and 2) the categorical type (i.e., decision tree, logistic regression, and neural network) will vary based on the packet counts or signal strength threshold that is dictated by the threshold criterion. There are at least two future research areas that correspond to this body of work: 1) there is a need to leverage the extended horizontal and vertical polarization null point model on multiple DSRC RSUs along a highway on/off ramp, and 2) there is a need to apply and validate different electric and magnetic (or propagation) models.

L'ammoniac atmosphérique (NH3) est émis en majeure partie par l'agriculture. Etant très soluble, il se dépose rapidement sur la végétation par absorption foliaire et dépôt sur les surfaces (dépôt cuticulaire). Ces dépôts constituent une source de pollution importante pour les écosystèmes dits sensibles. Afin d'étudier la variabilité des dépôts secs d'ammoniac à proximité des sources agricoles, en réponse aux conditions climatiques et au type d'écosystème, un modèle mécaniste de dispersion et de dépôt d'NH3 a été développé. Il repose sur le couplage d'un modèle de dispersion de gaz-traces, de type marche aléatoire, et d'un modèle d'échange à l'échelle foliaire prenant en compte les voies stomatiques et cuticulaires, et incluant le point de compensation stomatique. Le modèle a été validé à partir de données expérimentales mesurées sur un couvert de maïs et de deux autres jeux de données sur prairie. Le modèle simule bien les concentrations dans le cas de la prairie mais il est biaisé dans le cas du maïs. Le biais semble provenir de l'orientation moyenne de la direction du vent et met en avant la nécessité d'utiliser un modèle en 3 dimensions pour l'étude de la dispersion à l'échelle locale. L'application du modèle montre que les dépôts secs cumulés peuvent varier entre quelques dixièmes de % et quasiment 20% de la quantité émise à 400 m en aval d'une source ligne. Le modèle indique que les facteurs les plus influents sur le dépôt sont la hauteur de la source par rapport au couvert, la vitesse du vent et la stratification thermique, ainsi que les résistances stomatiques et cuticulaires. Sous un climat chaud et sec, le dépôt sec local d'ammoniac emprunte prioritairement la voie stomatique, tandis que sous un climat tempéré et humide, il se fait par voie cuticulaire. Il en ressort que pour améliorer les estimations du dépôt sec local, il sera nécessaire de mieux comprendre et paramétrer le dépôt cuticulaire, et le point de compensation stomatique.

The hole-doped perovskite manganese oxide such as Ln1-xAxMnO3 (Ln = La, Nd, Pr, and A = Ca, Sr, Ba, Pb) is one of the most studied topics in the recent years due to the observation of colossal magnetoresistance (CMR). Basically, LaMnO3 has an almost insulating behavior and on antiferromagnetic arrangement. By substituting a divalent cation (A2+) in place of La3+, LaMnO3 can be driven into metallic and ferromagnetic state. Mixed valence of Mn 3+ / Mn4+ is needed for both metallic behavior and ferromagnetism in these materials. The CMR characteristic occurs in the ferromagnetic state. A systematic investigation of the structural, magnetic and electrical properties in the perovskite colossal magnetoresistance materials La0.7-xLnxPb0.3Mn1-yMeyO3 (Ln=Pr, Nd and Y, Me=Fe and Co) has presented in this thesis. By subatituting Nd, Pr, Y for the La and Co, Fe for the Mn, the substitution effects on the crystallographic deformation, magnetotransport behavior and electrical properties in these compounds have been studied. According to the results of this research, crystallographic distortion is induced by the substitution of smaller ions, Pr or Nd, onto the La-site. Powder $x$-ray diffraction patterns show a crystallographic transition from rhombohedral symmetry (R-3c) to orthorhombic (Pbnm) crystal structure as the doping content is increased. The increase of deformation from R-3c to Pbnm decreases the bond angle of Mn3+¡ÐO2-¡ÐMn4+ , increases the cant of Mn spin, weakens the double-exchange interaction and results in decrease of ferromagnetism, low ferromagnetic transition temperature Tc, eg electron bandwidth and conductivity. However, the great quantity of decrease in resistivity by an external field leads to the increase in the magnetoresistance ratio. We also find that the increase of saturation magnetization results from the contribution of magnetic ion of Pr or Nd. In addition. in contrast to substitution La by magnetic ion of Pr and Nd, the saturation magnetization is decreased as Y content is increased. The zero-field-cool (ZFC) and field-cool (FC) magnetic measurements indicate that the range of spin ordering for Y one is shorter than Pr one or Nd one with the same doping content. It is because of the small ionic radius of Y, which results in larger distortion, increases the bond angle of Mn3+¡ÐO2-¡ÐMn4+, and corresponds low ferromagnetic transition temperature. The distortion induced by Mn-site substitution is not obvious due to the similar radius of Mn, Co and Fe. Powder x-ray diffraction patterns show a single phase of rhombohedral symmetry (R-3c) for Co doped ststem and a slight crystallographic transition from rhombohedral (R-3c) to orthorhombic (Pbnm) symmetry for Fe doped system. Values of temperature dependence of magnetization indicate that the ferromagnetic double-exchange interaction is gradually substituted by the superexchange interaction. The ZFC-FC curves also indicate that long-range spin ordering is progressively substituted by the short-range spin ordering. The substitution of Mn by Co and Fe supresses the double-exchange interaction, decreases the ferromagnetism and the ferromagnetic transition temperature. Due to the synthesis of the substitution of Nd, Pr, Y for La and Co, Fe for Mn, the mechanism of substitution effects are proved different. The substitution of Nd, Pr and Y for La distorts the crystal, decreases the Mn3+¡ÐO2-¡ÐMn4+ bond angle, and results in the transition of properties, while the substitution of Co and Fe for Mn decrease the percentage of ferromagnetic Mn3+¡ÐO2-¡ÐMn4+. The purpose of this thesis is to clear up the role functions of all elements in these compounds and properties of these compounds. Based on the knowledge of these compounds, it would be helpful to control the physical mechanism and improve the characteristics on preparing their thin film devices.

Adhesion between solid bodies plays a prominent role in a wide variety of situations ranging from tribological applications to dust coagulation initiating the formation of planets. It can be due to various reasons like capillary, electrostatic, van der Waals, and hydrophobic forces. Among these, adhesion due to van der Waals force| which has its origin in permanent or instantaneous electric dipoles present in all atoms and molecules|is of special significance as it is present in all cases. Computational studies on adhesion due to van der Waals force commonly assume it as a surface force due to its short effective range, which is about a few tens of nanometers, in comparison to the length-scales commonly encountered. However, such restrictions are often violated in various important problems. For example, the characteristic dimensions of asperities| which are the smallest roughness elements interacting to cause friction and wear| are usually of nanometer length-scale. In addition, the assumptions inherent in development of surface force model are exact only when the deformations are small. In all such situations, the van der Waals force must be assumed as distributed over the volume. In this work, a computational model is developed by incorporating van der Waals force and short-range repulsion (steric repulsion or Pauli repulsion) as body forces distributed over the volume in a large deformation, static/transient, finite element framework. First the development of the general formulation is discussed, and then it is specialized for various considerations like handling symmetry and interaction between an elastic body and a rigid half-space, which offer significant computational advantages over the general formulation. The applicability of the model is illustrated by using a number of benchmark and practical problems. The comparison of the analysis results and well-established analytical models are provided, which validates our method. As a specific example, the smooth change of interaction force from a thin-rod model to a at-plate model on increasing the cross-sectional areas of two interacting elastic rods is demonstrated. The impact of elastic bodies in presence adhesion, and the associated energy loss is an important concern in studies regarding the origin of friction. Therefore, adhesive impact of elastic rods and spheres is studied using our formulation. Emphasis of the study is on finding the apparent energy loss during impact, which represents the part of energy lost to elastic stress waves remaining in the body after the impact, and hence not available for rebound motion. In case of impact of elastic rods on a rigid half-space, it is shown that the apparent energy loss is a unique function of the tensile strain energy developed in the rod due to van der Waals attraction. A one-dimensional model is developed for this case to determine the energy loss based on the specified problem parameters, which can be used to predict practically relevant phenomena like capture. In case of impact of elastic spheres, which is often correlated with asperity interactions, the energy loss is found to be significant only if adhesion-induced instabilities occur. The behavior shown by rods and spheres are probably at the two extremes with regards to energy loss during impact of elastic bodies in presence of adhesion. Practical use of the formulation is demonstrated by applying it to the study of amplitude variation and phase shifts in tapping-mode atomic force microscopy. Specifically, the advantage of operating the AFM cantilever just below its natural frequency as compared to operating it just above the natural frequency is demonstrated. Bistable behavior, which is the coexistence of two stable vibration modes under exactly same operating conditions, is shown to be severe when the driving frequency is higher than the natural frequency of AFM cantilever even in the absence of adhesion, which can result in spurious contrast-reversal artifacts during imaging. The hysteresis loop associated with the bistable behavior may lead to erroneous conclusions regarding presence of adhesion. Since this model overcomes the limitations of lumped parameter models and the computational models based on surface force approximation, the results can be used for much more realistic interpretation of experimental data. Computational framework developed in this study achieves the capability for analysis of adhesive contact problems directly from van der Waals interaction and steric repulsion. Such a model can be used for revisiting the fundamental problems in contact mechanics, as well as for providing better insights into experimental observations.

The Master’s Thesis deals with a ZigBee technology and its devices working each other in wireless personal area network. The ZigBee and its advantages are compared with other wireless protocols working in ISM bands. A first part deals with a topology of IEEE 802.4.15 WPAN and the ZigBee features. Types and format of the ZigBee data-frame are mentioned. A Second part of this thesis describes a design and testing of the ZigBee devices. Results of this thesis are demonstrated on ZMU (ZigBee Modules Utility) program that has been developed for the testing of this technology.

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Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.