In this thesis we study the key requirements and solutions for the feasibility and application of Ethernet-TCP/IP technology to the networks we termed Highly-Variable Real-Time Networks (HVRN). This particular class of networks poses exceptionally demanding requirements because their physical and logical topologies are both temporally and spatially variable. We devised and introduced specific mechanisms for applying Ethernet-TCP/IP to HVRNs with particular emphasis on effective and reliable modular connectivity. Using a railroad train as a reference, this work analyzes the unique requirements of HVRNs and focuses on the backbone architecture for such a system under Ethernet and TCP/IP.

Given the rapid aging of the worldâ s population, improvements in technology for automation of patient care and documentation are badly needed. This project is based on previous research that demonstrated a â smartâ bed that can non-intrusively monitor a patient in bed and determine a patient's respiration, heart rate and movement without intrusive or restrictive medical measurements. The â smartâ bed is an application of spatially distributed integrating fiber optic sensors. The basic concept is that any patient movement that also moves an optical fiber within a specified area will produce a change in the optical signal. A statistical mode (STM) sensor and a high order mode excitation (HOME) sensor were previously investigated, based on which the author developed the present design including both modal modulation approaches. Development was made in both hardware and software for the combined STM/HOME sensor: a special lens system was installed allowing only the high order modes of the optical fiber to be excited and coupled into the sensor; computer-processing method was used for handling output from the dual STM-HOME sensor, which would offer comprehensive perturbation analysis for more reliable patient monitoring. Experimental results of simulating human body breathing and heartbeats by periodic mechanical perturbations are also presented, and the relative advantage and drawbacks of the two modal modulation approaches are discussed. / Master of Science

The purpose of this project is to develop a software framework, a 3D engine, which will generate images to be projected onto facets of a spatially immersive display (SID). The goal is to develop a software library to support the creation of images of specified 3D environments which are specific to the display geometries of a polyhedral class of SIDs. Part of this goal is developing auxiliary software to allow this library to be thoroughly tested. When properly working, the images being displayed on adjoining faces of the SID appear spatially and temporally consistent with one another, creating the illusion that the user is within a surrounding three-dimensional space.

The purpose of this project is to develop a software framework, a 3D engine, which will generate images to be projected onto facets of a spatially immersive display (SID). The goal is to develop a software library to support the creation of images of specified 3D environments which are specific to the display geometries of a polyhedral class of SIDs. Part of this goal is developing auxiliary software to allow this library to be thoroughly tested. When properly working, the images being displayed on adjoining faces of the SID appear spatially and temporally consistent with one another, creating the illusion that the user is within a surrounding three-dimensional space.

In this research, a recent version of the Boreal Ecosystem Productivity Simulator (BEPS), called BEPS-TerrainLab, was adapted to northern peatlands and evaluated using observations made at the Mer Bleue bog located near Ottawa, Ontario, and the Sandhill fen located near Prince Albert, Saskatchewan. The code was extended and modified with a major focus on the adequate representation of northern peatlands' multi-layer canopy and the associated processes related to energy, water vapour and carbon dioxide fluxes through remotely-sensed leaf area index (LAI) maps. An important prerequisite for the successful mapping of LAI based on remote sensing imagery is the accurate measurement of LAI in the field with a standard technique such as the LAI-2000 plant canopy analyzer. As part of this research, a quick and reliable method to determine shrub LAI with the LAI-2000 instrument was developed. This method was used to collect a large number of LAI data at the Mer Bleue bog for the development of a new remote sensing-based methodology using multiple endmember spectral unmixing that allows for separate tree and shrub LAI mapping in ombrotrophic peatlands. A slight modification of this methodology allows for its application to minerotrophic peatlands and their surrounding landscapes. These LAI maps were used to explicitly represent the tree and shrub layers of the Mer Bleue bog and the tree and shrub/sedge layers of the Sandill fen within BEPS-TerrainLab. The adapted version of BEPS-TerrainLab was used to investigate the in fluence of mescoscale topography (Mer Bleue bog) and macro- and mesoscale topography (Sandhill fen) on wetness, evapotranspiration, and gross primary productivity during the snow-free period of 2004. This research suggests that future peatland ecosystem modelling efforts at regional and continental scales should include a peatland type-specific differentiation of macro- and mesoscale topographic effects on hydrology, to allow for a more realistic simulation of peatlands' soil water balance. This is an important prerequisite for the reduction of currently existing uncertainties in wetlands' contribution to North America's carbon dioxide and methane annual fluxes from an ecosystem modelling perspective.

High-throughput sequencing has greatly influenced the amount of data produced and biological questions asked and answered. Sequencing approaches have also enabled rapid development of related technological fields such as single-cell and spatially resolved expression profiling. The introductory parts of this thesis give an overview of the basic molecular and technological apparatus needed to analyse the transcriptome in cells and tissues. This is succeeded by a summary of present investigations that report recent advancements in RNA profiling. RNA integrity needs to be preserved for accurate gene expression analysis. A method providing a low-cost alternative for RNA preservation was reported. Namely, a low concentration of buffered formaldehyde was used for fixation of human cell lines and peripheral blood cells (Paper I). The results from bulk RNA sequencing confirmed gene expression was not negatively impacted with the preservation procedure (r2&gt;0.88) and that long-term storage of such samples was possible (r2=0.95). However, it is important to note that a small population of cells overexpressing a limited amount of genes can skew bulk gene expression analyses making them sufficient only in carefully designed studies. Therefore, gene expression should be investigated at the single cell resolution when possible. A method for high-throughput single cell expression profiling termed microarrayed single-cell sequencing was developed (Paper II). The method incorporated fluorescence-activated cell sorting, sample deposition and profiling of thousands of barcoded single cells in one reaction. After sample attachment to a barcoded array, a high-resolution image was taken which linked the position of each array barcode sequence to each individual deposited cell. The cDNA synthesis efficiency was estimated at 17.3% while detecting 27,427 transcripts per cell on average. Additionally, spatially resolved analysis is important in cell differentiation, organ development and pathological changes. Current methods are limited in terms of throughput, cost and time. For that reason, the spatial transcriptomics method was developed (Paper III). Here, the barcoded microarray was used to obtain spatially resolved expression profiles from tissue sections using the same imaging principle. The mouse olfactory bulb was profiled on a whole-transcriptome scale and the results showed that the expression correlated well (r2=0.94-0.97) as compared to bulk RNA sequencing. The method was 6.9% efficient, reported signal diffusion at ~2 μm and accurately deconvoluted layer-specific transcripts in an unbiased manner. Lastly, the spatial transcriptomics concept was applied to profile human breast tumours in three dimensions (Paper IV). Unbiased clustering revealed previously un-annotated regions and classified them as parts of the immune system, providing a detailed view into complex interactions and crosstalk in the whole tissue volume. Spatial tumour classification divulged that certain parts of the tumour clearly classified as other subtypes as compared to bulk analysis providing useful data for current practice diagnostics. The last part of the thesis discusses a look towards the future, how the presented methods could be used, improved upon or combined in translational research. / <p>QC 20170109</p>

Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour lObservation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). <p>Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005.

Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour lObservation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). <p>Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005.

An agent based-model of mobility of pastoralists was developed and applied to the semi-arid rangeland region extending from southern Ethiopia to northern Kenya. This model was used to investigate temporal adaptation of pastoralists to the spatial heterogeneity of their environment. This dissertation describes the development, structure, and corroboration process of the simulation model, Pastoral Livestock Movement Model (PLMMO). PLMMO is a spatially explicit, individual-based pastoralists-animal foraging and movement model. It simultaneously simulates the foraging and movement behavior of individual pastoralists and their livestock in a rangeland ecosystem. Pastoralists?? herd mobility patterns and other measures of movement were compared to data from field studies. Predictions of the model correspond to observed mobility patterns across seasons. The distances moved were found to be significantly correlated (r2 = 0.927 to 0.977, p<0.0001) to drought and non-drought climatic regimes. The PLMMO model therefore proved to be a useful tool for simulating general movement patterns of pastoralists relative to movement range sizes in the pastoral rangelands of southern Ethiopia and northern Kenya. We then used the PLMMO model to explore the impact of emerging changes in rangeland use in the study area. The ways in which pastoralists?? mobility patterns adapt to emerging challenges in the study area were explored by simulating the following four scenarios: 1) climate change with concomitant reduction in forage yield, 2) climate change with concomitant improvement and higher variability in forage yield, 3) increased livestock population densities and 4) improved access to water. The climate induced change scenario with increased and more variable forage production resulted in the shortest distances moved by pastoralists in comparison to all other scenarios. The total search distances under this scenario were only 20% of normal season distances. The improved water access scenario also returned a significant (p=0.017) drop in distances moved. There was, however, no significant impact on either increase in livestock numbers or reduction in available forage on mobility. We judged the agent-based model PLMMO developed here as a robust system for emulating pastoral mobility in the rangelands of eastern Africa and for exploring the consequences of climate change and adaptive management scenarios.

In this research, a recent version of the Boreal Ecosystem Productivity Simulator (BEPS), called BEPS-TerrainLab, was adapted to northern peatlands and evaluated using observations made at the Mer Bleue bog located near Ottawa, Ontario, and the Sandhill fen located near Prince Albert, Saskatchewan. The code was extended and modified with a major focus on the adequate representation of northern peatlands' multi-layer canopy and the associated processes related to energy, water vapour and carbon dioxide fluxes through remotely-sensed leaf area index (LAI) maps. An important prerequisite for the successful mapping of LAI based on remote sensing imagery is the accurate measurement of LAI in the field with a standard technique such as the LAI-2000 plant canopy analyzer. As part of this research, a quick and reliable method to determine shrub LAI with the LAI-2000 instrument was developed. This method was used to collect a large number of LAI data at the Mer Bleue bog for the development of a new remote sensing-based methodology using multiple endmember spectral unmixing that allows for separate tree and shrub LAI mapping in ombrotrophic peatlands. A slight modification of this methodology allows for its application to minerotrophic peatlands and their surrounding landscapes. These LAI maps were used to explicitly represent the tree and shrub layers of the Mer Bleue bog and the tree and shrub/sedge layers of the Sandill fen within BEPS-TerrainLab. The adapted version of BEPS-TerrainLab was used to investigate the in fluence of mescoscale topography (Mer Bleue bog) and macro- and mesoscale topography (Sandhill fen) on wetness, evapotranspiration, and gross primary productivity during the snow-free period of 2004. This research suggests that future peatland ecosystem modelling efforts at regional and continental scales should include a peatland type-specific differentiation of macro- and mesoscale topographic effects on hydrology, to allow for a more realistic simulation of peatlands' soil water balance. This is an important prerequisite for the reduction of currently existing uncertainties in wetlands' contribution to North America's carbon dioxide and methane annual fluxes from an ecosystem modelling perspective.