Development and Experimental Analysis of Wireless High Accuracy Ultra-Wideband Localization Systems for Indoor Medical Applications

This dissertation addresses several interesting and relevant problems in the field of wireless technologies applied to medical applications and specifically problems related to ultra-wideband high accuracy localization for use in the operating room. This research is cross disciplinary in nature and fundamentally builds upon microwave engineering, software engineering, systems engineering, and biomedical engineering. A good portion of this work has been published in peer reviewed microwave engineering and biomedical engineering conferences and journals. Wireless technologies in medicine are discussed with focus on ultra-wideband positioning in orthopedic surgical navigation. Characterization of the operating room as a medium for ultra-wideband signal transmission helps define system design requirements. A discussion of the first generation positioning system provides a context for understanding the overall system architecture of the second generation ultra-wideband positioning system outlined in this dissertation. A system-level simulation framework provides a method for rapid prototyping of ultra-wideband positioning systems which takes into account all facets of the system (analog, digital, channel, experimental setup). This provides a robust framework for optimizing overall system design in realistic propagation environments. A practical approach is taken to outline the development of the second generation ultra-wideband positioning system which includes an integrated tag design and real-time dynamic tracking of multiple tags. The tag and receiver designs are outlined as well as receiver-side digital signal processing, system-level design support for multi-tag tracking, and potential error sources observed in dynamic experiments including phase center error, clock jitter and drift, and geometric position dilution of precision. An experimental analysis of the multi-tag positioning system provides insight into overall system performance including the main sources of error. A five base station experiment shows the potential of redundant base stations in improving overall dynamic accuracy. Finally, the system performance in low signal-to-noise ratio and non-line-of-sight environments is analyzed by focusing on receiver-side digitally-implemented ranging algorithms including leading-edge detection and peak detection. These technologies are aimed at use in next-generation medical systems with many applications including surgical navigation, wireless telemetry, medical asset tracking, and in vivo wireless sensors

Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) techniques have been employed, no research compares their performances and, more importantly, the benefits of combining both methods. Therefore, this paper proposes a framework that uses non-feature reduction (All-F) as a baseline to investigate the performance of FS, FX, and a combination of both (FSX). The case study employs the vegetation condition index (VCI)/temperature condition index (TCI) to develop 21 rice yield forecasting models for eight sub-regions in Vietnam based on ML methods, namely linear, support vector machine (SVM), decision tree (Tree), artificial neural network (ANN), and Ensemble. The results reveal that FSX takes full advantage of the FS and FX, leading FSX-based models to perform the best in 18 out of 21 models, while 2 (1) for FS-based (FX-based) models. These FXS-, FS-, and FX-based models improve All-F-based models at an average level of 21% and up to 60% in terms of RMSE. Furthermore, 21 of the best models are developed based on Ensemble (13 models), Tree (6 models), linear (1 model), and ANN (1 model). These findings highlight the significant role of FS, FX, and specially FSX coupled with a wide range of ML algorithms (especially Ensemble) for enhancing the accuracy of predicting crop yield

Satellite Observations for Identifying Continental-Scale Climate Change over Australia

Australia’s large extent and relatively low population density, as well as its range of climates, means that it is heavily dependent upon satellite observations to identify the extent and magnitude of climate change. This work examines three types of satellite missions that are used to assess different aspects of climate change. The first involves the use of radio occultation measurements based on signals from Global Navigation Satellite Systems (GNSS) spacecraft made by low-Earth orbiting (LEO) satellites to identify changes in the height of the tropopause, a sensitive indicator of climate change owing to its response to temperature changes in the troposphere and lower stratosphere. The second deals with rainfall over Australia, as measured by the Tropical Rainfall Monitoring Mission (TRMM), in conjunction with other satellite- and ground-based observations. Such observations are invaluable, given the scarcity of ground-based observations over vast areas of Australia.While a comparison between the TRMM product and existing ground-based data is very good, there appears to be a decrease in the correlation between datasets, the reason for which is still being investigated. Finally, we examine the state of terrestrial water storage over Australia as determined from variations in the regional gravity field as measured by the Gravity Recovery and Climate Experiment (GRACE) twin-satellite mission. The loss of substantial volumes of ground water from the Murray-Darling River Basin in the southeast corner of the continent is very apparent, as is an increase over the northern parts of the country. Together, such satellite missions provide a continental-scale picture of climate change over Australia, with temperature and rainfall variations, as well as water resources, able to be monitored, providing valuable information to natural resource managers and climate modellers who endeavour to predict future changes

Water storage changes and climate variability within the Nile Basin between 2002-2011

Understanding water storage changes within the Nile’s main sub-basins and the related impacts of climate variability is an essential step in managing its water resources. The Gravity Recovery And Climate Experiment (GRACE) satellite mission provides a unique opportunity to monitor changes in total water storage (TWS) of large river basins such as the Nile. Use of GRACE-TWS changes for monitoring the Nile is, however, difficult since stronger TWS signals over the Lake Victoria Basin (LVB) and the Red Sea obscure those from smaller sub-basins making their analysis difficult to undertake. To mitigate this problem, this study employed Independent Component Analysis (ICA) to extract statistically independent TWS patterns over the sub-basins from GRACE and the Global Land Data Assimilation System (GLDAS) model. Monthly precipitation from the Tropical Rainfall Measuring Mission (TRMM) over the entire Nile Basin are also analysed by ICA. Such extraction enables an in-depth analysis of water storage changes within each sub-basin and provides a tool for assessing the influence of anthropogenic as well as climate variability caused by large scale ocean–atmosphere interactions such as the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD).Our results indicate that LVB experienced effects of both anthropogenic and climate variability (i.e., a correlation of 0.56 between TWS changes and IOD at 95% confidence level) during the study period 2002–2011, with a sharp drop in rainfall between November and December 2010, the lowest during the entire study period, and coinciding with the drought that affected the Greater Horn of Africa. Ethiopian Highlands (EH) generally exhibited a declining trend in the annual rainfall over the study period, which worsened during 2007–2010, possibly contributing to the 2011 drought over GHA. A correlation of 0.56 was found between ENSO and TWS changes over EH indicating ENSO’s dominant influence. TWS changes over Bar-el-Ghazal experienced mixed increase–decrease, with ENSO being the dominant climate variability in the region during the study period. A remarkable signal is noticed over the Lake Nasser region indicating the possibility of the region losing water not only through evaporation, but also possibly through over extraction from wells in the Western Plateau (Nubian aquifer)

GRACE Hydrological Monitoring of Australia: Current Limitations and Future Prospects

The Gravity Recovery and Climate Experiment (GRACE) twin-satellite gravimetry mission has been monitoring time-varying changes of the Earth's gravitational field on a near-global scale since 2002. One of the environmentally important signals to be detected is temporal variations induced by changes in the distribution of terrestrial water storage (i.e., hydrology).Since water is one of Australia's precious resources, it is logical to monitor its distribution, and GRACE offers one such opportunity. The second and fourth releases (referred to as RL02and RL04) of the 'standard' monthly GRACE solutions with respect to their annual mean are analysed. When compared to rainfall data over the same time period, GRACE is shown to detect hydrological signals over Australia, with the RL04 data showing better results. However, the relatively small hydrological signal typical for much of Australia is obscured by deficiencies in the standard GRACE data processing and filtering methods. Spectral leakage of oceanic mass changes also still contaminates the small hydrological signals typical over land. It is therefore recommended that Australia-focussed reprocessing of GRACE data is needed for useful hydrological signals to be extracted. Naturally,this will have to be verified by independent 'insitu' external sources such as rainfall, soil moisture and groundwater bore hole piezometer data over Australia

On the suitability of the 4° × 4° GRACE mascon solutions for remote sensing Australian hydrology

Hydrological monitoring is essential for meaningful water-management policies and actions, especially where water resources are scarce and/or dwindling, as is the case in Australia. In this paper, we investigate the regional 4° × 4° mascon (mass concentration) GRACE solutions for Australia provided by GSFC (Goddard Space Flight Center, NASA) for their suitability in monitoring Australian hydrology, with a particular focus on the Murray-Darling Basin (MDB). Using principal component analysis (PCA) and multi-linear regression analysis (MLRA), the main components of spatial and temporal variability in the mascon solutions are analysed over the whole Australian continent and the MDB. The results are compared to those from global solutions provided by CSR (Center for Space Research, University of Texas at Austin, USA) and CNES/GRGS (Centre National d'Études Spatiales/Groupe de Recherche de Geodesie Spatiale, France) and validated using data from the Tropical Rainfall Measuring Mission (TRMM), water storage changes predicted by the WaterGap Global Hydrological Model (WGHM) and the Global Land Data Assimilation System (GLDAS), and ground-truth (river-gauge) observations.For the challenging Australian case with generally weak hydrological signals, the mascon solutions provide similar results to those from the global solutions, with the advantage of not requiring additional filtering (destriping and smoothing) as, for example, is necessary for the CSR solutions. A further advantage of the mascon solutions is that they offer a higher temporal resolution (i.e., 10 days) compared to approximately monthly CSR solutions. Examining equivalent water volume (EWV) time series for the MDB shows a good cross-correlation (generally > 0.7) among the GRACE solutions when considering the whole basin, although lower (generally 0.6), with all time series appearing to visually follow the general behaviour of the river-gauge data, although the cross-correlations are relatively low (between 0.3 and 0.6).Research Highlights ► Mascon provides equivalent results as global CSR & CNES/GRGS solutions. ► All examined GRACE releases reveal seasonal & tropical north signals. ► GRACE, modelled hydrology & precipitation show similar behaviour Australia wide. ► GRACE solutions generally follow river gauge data

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability

Progress Towards the New Australian Geoid-type Model as a Replacement for AUSGeoid98

We are nearing the final stages of producing a new geoid-type model for Australia that will replace AUSGeoid98. The terminology geoid-type reflects that the gravimetric quasigeoid model will be fitted to Australia-wide GPS-levelling data, probably using least-squares collocation. This will provide a user-friendly product for the more direct transformation of GPS-derived ellipsoidal heights to normal-orthometric heights on the Australian Height Datum (AHD). This has become necessary because Australian government geodetic authorities have decided to retain the AHD for the 'foreseeable future', whereas it is well known that the AHD contains about 1-2m distortions mainly due to fixing the AHD height to zero at 32 tide gauges. Another driver is that there is an increasing trend towards establishing vertical control using carrier-phase GPS via the single-point precise point positioning (PPP) technique or over very long baselines using the AUSPOS on-line service. When the quasigeoid model was used with differential GPS over short baselines, common/correlated errors cancelled in this relative mode, whereas they do not in the absolute or long-baseline modes. As such, AUSPOS and PPP users of AUSGeoid98 can sometimes find up to 2m discrepancies with existing AHD benchmarks. In addition, we will use improved quasigeoid modelling techniques and the most recent datasets available, such as GRACE (Gravity Recovery and Climate Experiment) global gravity field models, satellite-altimeter-derived gravity anomalies in marine areas that have been re-tracked to improve them in the coastal zone, the latest cleaned release of the Australian land gravity database, the version 2 Australian digital elevation model, which now allows the computation of nine arc-second resolution topographical effects. Some emphasis will be placed on the use of modified kernels as high-pass filters to manage long-wavelength errors in the Australian terrestrial gravity and terrain data, so that they do not contaminate the high-quality GRACE data

Structural basis of severe acute respiratory syndrome coronavirus ADP-ribose-1''-phosphate dephosphorylation by a conserved domain of nsP3.

The crystal structure of a conserved domain of nonstructural protein 3 (nsP3) from severe acute respiratory syndrome coronavirus (SARS-CoV) has been solved by single-wavelength anomalous dispersion to 1.4 A resolution. The structure of this "X" domain, seen in many single-stranded RNA viruses, reveals a three-layered alpha/beta/alpha core with a macro-H2A-like fold. The putative active site is a solvent-exposed cleft that is conserved in its three structural homologs, yeast Ymx7, Archeoglobus fulgidus AF1521, and Er58 from E. coli. Its sequence is similar to yeast YBR022W (also known as Poa1P), a known phosphatase that acts on ADP-ribose-1''-phosphate (Appr-1''-p). The SARS nsP3 domain readily removes the 1'' phosphate group from Appr-1''-p in in vitro assays, confirming its phosphatase activity. Sequence and structure comparison of all known macro-H2A domains combined with available functional data suggests that proteins of this superfamily form an emerging group of nucleotide phosphatases that dephosphorylate Appr-1''-p