Signal-to-noise ratio (SNR) estimation is one of the important research topics in wireless communications. In the receiver, many algorithms require SNR information to achieve optimal performance. In this thesis, an autocorrelation based SNR estimator is proposed. The proposed method utilizes the correlation properties of symbol sequence and the uncorrelated properties of noise sequence to distinguish the signal power from the received signal. Curve fitting method is used for SNR estimator to predict the signal power. Mean and variance performance of the proposed SNR estimator is compared with that of the conventional SNR estimator by computer simulations. These simulations consider additive white Gaussian noise and multipath Rayleigh fading channel with BPSK, 8PSK, 16QAM and 64QAM modulation schemes. According to the simulation results, the proposed method can provide better performance than conventional methods in both mean and mean-square-error.

No description available.

The goal of this thesis is to study and model the Diel Vertical Migration (DVM) pattern using machine learning methods. We choose an Almost Periodic Function as the mathematical model and fit the monthly averaged migration data into a 5-term Fourier series whose coefficients and frequency are functions of time. The resulting function captures the general characteristics of the DVM pattern whose period is similar yet undergoes gradual changes over time. Further correlation analyses show that the monthly averaged distribution of zooplankton and various environmental factors are strongly correlated. Therefore, we adjust the function so that the coefficients and frequency are functions of environmental factors. Besides, we also examine the pattern on finer time scales using classification algorithms. We build classifiers which predict zooplankton existence at different depths based on a set of environmental measurements. Experiments demonstrate that both of the above methods are valid in modeling the DVM pattern.

Thesis (M.A.)--University of Wyoming, 2008. / Title from PDF title page (viewed on Mar. 23, 2010). Includes bibliographical references (p. 93-94).

Forecasting tools play an important role in public response to epidemics. Despite this, limited work has been done in comparing best-in-class techniques across the broad spectrum of time series forecasting methodologies. Forecasting frameworks were developed that utilised three methods designed to work with nonlinear dynamics: Iterated Filtering (IF) 2, Hamiltonian MCMC (HMC), and S-mapping. These were compared in several forecasting scenarios including a seasonal epidemic and a spatiotemporal epidemic. IF2 combined with parametric bootstrapping produced superior predictions in all scenarios. S-mapping combined with Dewdrop Regression produced forecasts slightly less-accurate than IF2 and HMC, but demonstrated vastly reduced running times. Hence, S-mapping with or without Dewdrop Regression should be used to glean initial insight into future epidemic behaviour, while IF2 and parametric bootstrapping should be used to refine forecast estimates in time. / Thesis / Master of Science (MSc)

No description available.

Yes / The main objective of this research is to investigate solid-state destabilization mechanism and kinetics of the model cocrystal caffeine : malonic acid (CA:MO) in presence of oxalic acid (OX) as a structural competitor. Competitive destabilization of CA:MO and subsequent formation of CA:OX takes place at temperatures significantly below its melting point. Destabilization mechanism was found to be mediated by sublimation of both CA:MO and OX. During CA:MO destabilization, free CA could not be detected and direct transformation to CA:OX cocrystal was observed. The destabilization kinetics follow Prout-Tompkins nucleation and crystal growth model with activation energy of 133.91 kJ/mol and subsequent CA:OX growth kinetic follow Ginstling – Brounshtien diffusion model with activation energy of kJ/mol.

No description available.

Custom-designed hearing instruments comprise the majority of those dispensed in the United States today. Because of their custom nature, there has been no means of evaluating them until they have been completed. There would be advantages to evaluating custom instruments prior to their completion. This study investigates a means of evaluating custom instruments prior to their final assembly into the customized shell. This is done by having the subject listen to the circuitry of the instrument while it is still mounted on the faceplate, which is accomplished by coupling the faceplate assembly to the subject's ear with foam earplug. To determine if the faceplate assembly, when coupled to the subject's ear, is a facsimile of the completed instrument, the insertion gain of the faceplate assembly was compared to the insertion gain of the completed instrument. Real ear measurements were obtained for both conditions (faceplate assembly vs. custom instrument) on twelve subjects. Once insertion gain was measured, the faceplate assemblies were then converted into custom instruments and insertion gain remeasured. A two-way Analysis of Variance test revealed no significant difference between the two test conditions at five representative test frequencies. A tolerance template, as specified by ANSI S3.22 1982, was used as a second criterion for similarity between two conditions. The tolerance template was superimposed over the insertion gain curves of the twelve faceplate assembly conditions to determine if the insertion gain curves of the completed instrument fell within acceptable variances. None of the insertion gain curves for the completed instruments fell completely within the tolerances allowed by the template. Based on this criterion, it was concluded there was a significant difference between the insertion gain of the two conditions and therefore the faceplate assembly was not a facsimile of the completed instrument, where insertion gain was concerned. However, from a subjective standpoint the faceplate assembly might still have some utility in the fitting of the custom in-the-ear hearing instruments. For example, it could be used to allow potential hearing aid wearers to experience different technologies during the preselection phase of the fitting process.

This thesis has detailed the extensive analysis of the XAS and RIXS spectra of LiFePO4 and FePO4, with the primary focus on LiFePO4. One of the primary motivations for this study was to understand the electronic structure of the two compounds and, in particular, shed some light on the nature of electron correlation within the samples. Two classes of band structure calculations have come to light. One solution uses the Hubbard U parameter, and this solution exhibits a band gap of about 4 eV. Other solutions that use standard DFT electron correlation functionals yield band gaps between 0 and 1.0 eV. <p>The RIXS spectra of LiFePO4 and FePO4 were analyzed using Voigt function fitting, an uncommon practice for RIXS spectra. Each of the spectra was fit to a series of Voigt functions in an attempt to localize the peaks within the spectra. These peaks were determined to be RIXS events, and the energetic centers of these peaks were compared to a small band gap band structure calculation. The results of the RIXS analysis strongly indicate that the small gap solution is correct. This was a surprising result, given that LiFePO4 is an ionic, insulating transition metal oxide, showing all of the usual traits of a Mott-type insulator. <p>This contradiction was explained in terms of polaron formation. Polarons can severely distort the lattice, which changes the local charge density. This changes the local DOS such that the DOS probed by XAS or RIXS experiments is not necessarily in the ground state. In particular, polaron formation can reduce the band gap. Thus, the agreement between the small gap solution and experiment is false, in the sense that the physical assumptions that formed the basis of the small gap calculations do not reflect reality. Polaronic distortion was also tentatively put forward as an explanation for the discrepancy between partial fluorescence yield, total fluorescence yield, and total electron yield measurements of the XAS spectra of LiFePO4 and FePO4.