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The high latitude ionosphere is a dynamic region that is subject to a variety of disturbed conditions affecting oblique propagation of HF radio signals. Multipath and off great circle propagation resulting from rough ionospheric reflecting surfaces introduce time dispersion, while movements in the ionosphere impose Doppler dispersion. These effects, when combined, result in very complex signal characteristics, which cause degradation to the performance of communication systems if the delay or Doppler dispersion exceeds system dependent bounds.;Results are presented based around measurements of signals, produced by the Doppler And Multipath SOunding Network, recorded using a six channel spaced array receiving system, during a 10 day campaign at Kiruna in March 1998. In addition to delay and Doppler processing, the directions of arrival (DOA) of the signal components were estimated using a super resolution direction finding algorithm.;Most signals were narrow in delay and Doppler, however a significant statistical spread in their DOA was observed. Statistics on the occurrence and characteristics of the remaining signals revealed that trends in DOA are often associated with complex delay and Doppler propagation. A number of cases had Doppler spread exceeding a specified Doppler boundary of operation, while none exceeded the delay spread boundary.;Case studies revealed detailed propagation effects. Of particular interest is the often observed East → West trend in DOA with Doppler offset. This is attributed to scattering from irregularities embedded in turbulent bulk convection flows. E-region modes with good spectral and geographic distribution of signal energy are often observed in otherwise complex cases.;Spatial filtering, using two or three element arrays was investigated and found to be effective at reducing large Doppler spreads to lower values more amenable to high data rate communication systems. Fast solver spatial filtering methods were found to be very robust, effective and a lot quicker.

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The volume reduction in power converters over the past several decades can chiefly be attributed to increases in switching frequency. It is to be expected that the trends towards miniaturization will maintain steady pressure to keep this pace of increasing switching frequencies of power converters. However certain fundamental limits in high frequency power conversion are being reached as frequencies are being pushed deeper into the megahertz range, inhibiting substantial further increases. The work reported in this dissertation is intended to systematically investigate the fundamental frequency limitations, identify some of the solutions for HF/VHF power conversion and to provide guidelines and tools to optimize the performance of power converters by maximizing frequency. A number of multi-megahertz power converters are examined to evaluate the present status and future trend of HF/VHF power conversion. An interesting trend between power level and frequency is observed. A general limitation about the power level and frequency, independent of design details, is derived from the physics of the semiconductor devices, which determines the upper bound of the power levels as frequency increases. A 250 MHz DC-DC power converter (derived from the Class E power amplifier) is analyzed and demonstrated with discrete components, which again verifies the trend between power level and frequency. The power losses in the semiconductor devices are discussed, and optimization criteria for minimizing the power losses of the devices, are discussed. By relating the power losses to the semiconductor materials' properties, a methodology for selecting proper materials is identified for high frequency and high efficiency power conversion. The frequency scaling effects of passive components, still dominating the volume of the modern power converter, is analyzed. A generic multi-disciplinary methodology is developed to analyze and maximize frequency and performance of passive components in terms of power density and efficiency. It is demonstrated how the optimum frequency can be identified, and how power conversion efficiency deteriorates beyond this optimum under a fixed maximum temperature. Power loss measurement is becoming more challenging as higher frequency and higher efficiency power conversion. To achieve an accurate power loss measurement in a high frequency, high efficiency power electronics system or component, limitations of electrical measurement are identified, and various calorimetric methods are surveyed. Calorimetric methods are more accurate due to the direct heat loss measurement. An advanced calorimetric system is proposed, analyzed, and tested, demonstrating about 5% error in total losses up to 25W. / Ph. D.

The Gravity Recovery and Climate Experiment (GRACE) project has two primary goals: to determine the Earth’s mean gravitational field over the lifetime of the mission and to observe the time-variable nature of the gravitational field. The Center for Space Research's (CSR) Release 4 (RL04) GRACE solutions are currently created via a least-squares process that assimilates data collected over a month using a simple boxcar window and determines a spherical harmonic representation of the monthly gravitational field. The nature of this technique obscures the time-variable gravity field on time scales shorter than one month and spatial scales shorter than a few hundred kilometers. A computational algorithm is developed here that allows increased temporal resolution of the GRACE gravity information, thus allowing the Earth's time-variable gravity to be more clearly observed. The primary technique used is a sliding-window algorithm attached to a weighted version of batch least squares estimation. A number of different temporal windowing functions are evaluated. Their results are investigated via both spectral and spatial analyses, and globally as well as in localized regions. In addition to being compared to each other, the solutions are also compared to external models and data sets, as well as to other high-frequency GRACE solutions made outside CSR. The results demonstrate that a GRACE solution made from at least eight days of data will provide a well-conditioned solution. A series of solutions made with windows of at least that length is capable of observing the expected near-annual signal. The results also indicate that the signals at frequencies greater than 3 cycles/year are often smaller than the GRACE errors, making detection unreliable. Altering the windowing technique does not noticeably improve the resolution, since the spectra of the expected errors and the expected non-annual signals are very similar, leading any window to affect them in the same manner. / text

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Three main issues are explored in this thesis—volatility measurement, volatility spillover and large-dimension covariance matrices. For the first question of volatility measurement, this thesis compares two newly-proposed, high-frequency volatility measurement models, namely realized volatility and realized range-based volatility. It does so in the aim of trying to use empirical results to assess whether one volatility model is better than the other. The realized volatility model and realized range-based volatility model are compared based on three markets, five forecast models, two data frequencies and two volatility proxies, making sixty scenarios in total. Seven different loss functions are also used for the evaluation tests. This necessarily ensures that the empirical results are highly robust. After making some simple adjustments to the original realized range-based volatility, this thesis concludes that it is clear that the scaled realized range-based volatility model outperforms the realized volatility model. For the second research question on volatility spillover, realized range-based volatility and realized volatility models are employed to study the volatility spillover among the S&P 500 index markets, with the aim of finding out empirically whether volatility spillover exists between the markets. Volatility spillover is divided into the two categories of statistically significant volatility spillover and economically significant volatility spillover. Economically significant spillover is defined as spillover that can help forecast the volatility of another market, and is therefore a more powerful measurement than statistically significant spillover. The findings show that, in reality, the existence of volatility spillover depends on the choice of model, choice of volatility proxy and value of parameters used. The third and final research question in this thesis involves the comparison of various large-dimension multivariate models. The main contribution made by this specific study is threefold. First, a number of good performance multivariate volatility models are introduced by adjusting some commonly used models. Second, different models and various choices of parameters for these models are tested based on 26 currency pairs. Third, the evaluation criteria adopted possess much more practical implications than those used in most other papers on this subject area.