The increase in vehicular traffic has created new challenges in determining the behavior of performance of data and safety measures in traffic. Hence, traffic signals on intersection used as cost effective and time saving tools for traffic management in urban areas. But on the other hand the signalized intersections in congested urban areas are the key source of high traffic density and slow traffic. High traffic density causes the slow network traffic data rate between vehicle to vehicle and vehicle to infrastructure. To match up with the emerging technologies, LTE takes the lead with good packet delivery and versatile to changes in the network due to vehicular movements and density. This thesis is about analyzing of LTE implementation based on a road traffic density model. This thesis work is aimed to use probability distribution function to calculate density values and develop a real traffic scenario in LTE network using density values. In order to analyze the traffic behavior, Aimsun simulator software has been used to represent the real situation of traffic density on a model intersection. For a realistic traffic density model field measurement were used for collection of input data. After calibration and validation process, a close to realty results extracted and used a logistic curve of probability distribution function to find out the density situation on each part of intersection. Similar traffic scenarios were implemented on MATLAB based LTE system level simulator. Results were concluded with the whole traffic scenario of 90 seconds and calculating the throughput at every traffic signal time and section. It is quite evident from the results that LTE system adopts the change of traffic behavior with dynamic nature and allocates more bandwidth where it is more needed.

In the original Vasicek model interest rates are calculated assuming that volatility remains constant over the period of analysis. In this study, we constructed a stochastic volatility model for interest rates. In our model we assumed not only that interest rate process but also the volatility process for interest rates follows the mean-reverting Vasicek model. We derived the density function for the stochastic element of the interest rate process and reduced this density function to a series form. The parameters of our model were estimated by using the method of moments. Finally, we tested the performance of our model using the data of interest rates in Turkey.

Stochastic di®erential equations (SDEs) are　central to much of modern finance theory and have been widely used to model the behaviour of key variables such as the　instantaneous short-term interest rate, asset prices, asset returns and their volatility.　The explanatory and/or predictive power of these models depends crucially on the particularisation of the model SDE(s) to real data through the choice of values for their parameters. In econometrics, optimal parameter estimates are generally considered to　be those that maximise the likelihood of the sample. In the context of the estimation of the parameters of SDEs, however, a closed-form expression for the likelihood　function is rarely available and hence exact maximum-likelihood (EML) estimation is usually infeasible. The key research problem examined in this thesis is the development of generic, accurate and computationally feasible estimation procedures based on the　ML principle, that can be implemented in the absence of a closed-form expression for　the likelihood function. The overall recommendation to come out of the thesis is that　an estimation procedure based on the finite-element solution of a reformulation of the Fokker-Planck equation in terms of the transitional cumulative distribution function(CDF) provides the best balance across all of the desired characteristics. The recommended approach involves the use of an interpolation technique proposed in this thesis which greatly reduces the required computational effort.

Over the years `short-range order' (SRO), whereby the local atomic arrangement differs from that of a random distribution, has been used to explain physical phenomena such as thermodynamic discontinuities, increased strength, anomalous electrical resistivity and magnetic variations in a host of alloys. However, due mainly to experimental difficulties and the complexity of the calculations required for the analysis of diffuse scattering, such work has been largely abandoned and hence quantification and assessment of SRO is notably sparse in the literature. The recent development of reverse Monte-Carlo (RMC) methods for the analysis of total scattering data has opened a promising route for the assessment of a material's local environment and has already provided important insights into a host of complex chemical systems, including liquids, network glasses, nano-materials, functional oxides and metal organic frameworks. The work presented in this thesis focuses on the development of a new methodology for the analysis of local structural effects in metallic systems using total scattering, and the first systematic application to the study of alloys. The simulation of total scattering data from a range of model structures is used to show that the information content of total scattering functions, in particular the pair distribution function (PDF), is sufficiently high to allow the assessment of different types and degrees of short-range order. This is supported by a demonstration of how such orders can be quantified from large box models, produced by fitting total scattering data using the RMC algorithm, with the mathematical analyses outlined. This culminates in a proposed methodology for the analysis of SRO in alloys. Having developed this analytical methodology it is subsequently applied to a number of interesting alloy systems. To demonstrate the efficacy of this methodology it was first applied to the study of a sample of Cu$_{3}$Au - the classically cited case example of a system demonstrating SRO prior to an ordering transition. This experiment provides new insight into this well characterised transition, and also demonstrates the significance of data processing errors on the generation of artefacts in large box modelling. The technique is also applied to the study of the industrially important family of nickel superalloys, assessing ordering in the gamma-phase alloy Ni-Cr and the sublattice orderings occurring in L1$_{2}$ alloys. Next, the use of the technique for the analysis of local strains exhibited in a lattice is presented. A series of models is used to demonstrate how the PDF is expected to change under variations in local strain caused by increased concentration of atomic substitution and variation in atomic radii. This is subsequently used to study the characteristic high-entropy alloy (HEA) CrMnFeCoNi. Through analysis of the PDF, it is demonstrated that the level of local strain exhibited in this alloy is not significantly different from those of other related compositionally simpler alloys. This result is highly significant as it challenges one of the core principles of the field - that the lattices of HEAs are necessarily highly strained. Finally, the energetics of ordering reactions are briefly considered and used to justify some of the observed transformations presented in the earlier work. Together, the body of work in this thesis shows how the total scattering technique can be used to provide valuable insight into a host of interesting local phenomena occurring in alloy systems. It is hoped that this will open up a new field of study into these effects, and ultimately guide the creation of new alloys based on these structure-property relationships.

Motivated by a study of social spider behavior, we discuss the distribution of the distances from all the events in a spatial point pattern to a point of reference that has a known location at a given moment of time. The distribution depends on both the shape of the region and the location of the point of reference. The empirical CDF is used to describe the distribution of the distances and compare it to the CDF derived under complete spatial randomness. Empirical distributions are then compared through time focusing on the case in which the point of reference changes with time.

An investigation into the relaxation of highly vibrationally excited trifluorobenzene and tetrafluorobenzene following collisions with carbon dioxide was performed using diode laser transient absorption spectroscopy. A 248 nm excimer laser prepared the vibrationally hot (E'~41,000 cm-1) fluorobenzene molecules. Large amounts of translational and rotational energy are transferred through collisions between the hot donor molecule and CO2. Rate constants and collisional probabilities were calculated by probing the high J states (J=58~80) of CO2 in the vibrational ground state, 0000, with measurements taken 1 µsec, ¼ the mean gas collision time, following each excimer laser pulse. The energy transfer probability distribution function, P(E,E'), was calculated for each molecule using the state-resolved probabilities and the energy gain of the bath. The study found a relationship between the fraction of strong collisions and the donor's dipole moment. Additionally, these findings support an application of Fermi's Golden rule to collisional energy transfer by linking the shape of P(E,E') to the shape of the donor's density of states as a function of ΔE.

This study intends to build up new techniques for how to obtain completely data-driven choices of the smoothing parameter in functional estimation, within the confines of minimal assumptions. The focus of the study will be within the framework of the estimation of the distribution function, the density function and their multivariable extensions along with some of their functionals such as the location and the integrated squared derivatives.

No description available.

A novel torque control algorithm, which adopts a two-phase excitation, is proposed to improve the performance of the Switched Reluctance Motor (SRM) drive. By exciting two adjacent phases instead of single phase, the changing rate and the magnitude of the phase currents are much reduced. Therefore the existing problems caused by the single-phase excitation such as large torque ripple during commutation, increased audible noise and fatigue of the rotor shaft are mitigated. The electromagnetic torque is efficiently distributed to each phase by the proposed Torque Distribution Function (TDF) that also compensates the effects of mutual coupling. To describe the effects of mutual coupling between phases, a set of voltage and torque equations is newly derived for the two-phase excitation. Parameters of the SRM are obtained by Finite Element Analysis (FEA) and verified by measurements. It is shown that the mutual inductance of two adjacent phases partly contributes to generate the electromagnetic torque and introduces coupling between two adjacent phases in the current or flux linkage control loop, which has been neglected in the single-phase excitation. The dynamics of the current or flux linkage loop are coupled and nonlinear due to the mutual inductance between two adjacent phases and the time varying nature of inductance. Each phase current or flux linkage needs to be controlled precisely to achieve the required performance. A feedback linearizing current controller is proposed to linearize and decouple current control loop along with a gain scheduling scheme to maintain performance of the current control loop regardless of rotor position as well as a feedback linearizing flux linkage controller. Finally, to reduce current or flux linkage ripple, a unipolar switching strategy is proposed. The unipolar switching strategy effectively doubles the switching frequency without increasing the actual switching frequency of the switches. This contributes to the mitigation of current or flux linkage ripple and hence to the reduction of the torque ripple. / Ph. D.

No description available.