Use of airborne laser scanner data in demanding forest conditions

Most of the applications of airborne laser scanner data to forestry require that the point cloud be normalized, i.e., each point represents height from the ground instead of elevation. To normalize the point cloud, a digital terrain model (DTM), which is derived from the ground returns in the point cloud, is employed. Unfortunately, extracting accurate DTMs from airborne laser scanner data is a challenging task, especially in tropical forests where the canopy is normally very thick (partially closed), leading to a situation in which only a limited number of laser pulses reach the ground. Therefore, robust algorithms for extracting accurate DTMs in low-ground-point-densitysituations are needed in order to realize the full potential of airborne laser scanner data to forestry. The objective of this thesis is to develop algorithms for processing airborne laser scanner data in order to: (1) extract DTMs in demanding forest conditions (complex terrain and low number of ground points) for applications in forestry; (2) estimate canopy base height (CBH) for forest fire behavior modeling; and (3) assess the robustness of LiDAR-based high-resolution biomass estimation models against different field plot designs. Here, the aim is to find out if field plot data gathered by professional foresters can be combined with field plot data gathered by professionally trained community foresters and used in LiDAR-based high-resolution biomass estimation modeling without affecting prediction performance. The question of interest in this case is whether or not the local forest communities can achieve the level technical proficiency required for accurate forest monitoring. The algorithms for extracting DTMs from LiDAR point clouds presented in this thesis address the challenges of extracting DTMs in low-ground-point situations and in complex terrain while the algorithm for CBH estimation addresses the challenge of variations in the distribution of points in the LiDAR point cloud caused by things like variations in tree species and season of data acquisition. These algorithms are adaptive (with respect to point cloud characteristics) and exhibit a high degree of tolerance to variations in the density and distribution of points in the LiDAR point cloud. Results of comparison with existing DTM extraction algorithms showed that DTM extraction algorithms proposed in this thesis performed better with respect to accuracy of estimating tree heights from airborne laser scanner data. On the other hand, the proposed DTM extraction algorithms, being mostly based on trend surface interpolation, can not retain small artifacts in the terrain (e.g., bumps, small hills and depressions). Therefore, the DTMs generated by these algorithms are only suitable for forestry applications where the primary objective is to estimate tree heights from normalized airborne laser scanner data. On the other hand, the algorithm for estimating CBH proposed in this thesis is based on the idea of moving voxel in which gaps (openings in the canopy) which act as fuel breaks are located and their height is estimated. Test results showed a slight improvement in CBH estimation accuracy over existing CBH estimation methods which are based on height percentiles in the airborne laser scanner data. However, being based on the idea of moving voxel, this algorithm has one main advantage over existing CBH estimation methods in the context of forest fire modeling: it has great potential in providing information about vertical fuel continuity. This information can be used to create vertical fuel continuity maps which can provide more realistic information on the risk of crown fires compared to CBH.

Real-time adaptive control of ultra-fast laser scribing process with spectrometer online monitoring

There exist several researches and applications about laser welding monitoring and parameter control but not a single one have been created for controlling of laser scribing processes. Laser scribing is considered to be very fast and accurate process and thus it would be necessary to develop accurate turning and monitoring system for such a process. This research focuses on finding out whether it would be possible to develop real-time adaptive control for ultra-fast laser scribing processes utilizing spectrometer online monitoring. The thesis accurately presents how control code for laser parameter tuning is developed using National Instrument's LabVIEW and how spectrometer is being utilized in online monitoring. Results are based on behavior of the control code and accuracy of the spectrometer monitoring when scribing different steel materials. Finally control code success is being evaluated and possible development ideas for future are presented.

Physiological reactivity and the perception of emotional stimuli as they relate to social adaptive functioning after traumatic brain injury / y Melonie Jane Hopkins.

Traumatic brain injury (TBI) often affects social adaptive functioning and these changes in social adaptability are usually associated with general damage to the frontal cortex. Recent evidence suggests that certain neurons within the orbitofrontal cortex appear to be specialized for the processing of faces and facial expressions. The orbitofrontal cortex also appears to be involved in self-initiated somatic activation to emotionally-charged stimuli. According to Somatic Marker Theory (Damasio, 1994), the reduced physiological activation fails to provide an individual with appropriate somatic cues to personally-relevant stimuli and this, in turn, may result in maladaptive behaviour. Given the susceptibility of the orbitofrontal cortex in TBI, it was hypothesized that impaired perception and reactivity to socially-relevant information might be responsible for some of the social difficulties encountered after TBL Fifteen persons who sustained a moderate to severe brain injury were compared to age and education matched Control participants. In the first study, both groups were presented with photographs of models displaying the major emotions and either asked to identify the emotions or simply view the faces passively. In a second study, participants were asked to select cards from decks that varied in terms of how much money could be won or lost. Those decks with higher losses were considered to be high-risk decks. Electrodermal activity was measured concurrently in both situations. Relative to Controls, TBI participants were found to have difficulty identifying expressions of surprise, sadness, anger, and fear. TBI persons were also found to be under-reactive, as measured by electrodermal activity, while passively viewing slides of negative expressions. No group difference,in reactivity to high-risk card decks was observed. The ability to identify emotions in the face and electrodermal reactivity to faces and to high-risk decks in the card game were examined in relationship to social monitoring and empathy as described by family members or friends on the Brock Adaptive Functioning Questionnaire (BAFQ). Difficulties identifying negative expressions (i.e., sadness, anger, fear, and disgust) predicted problems in monitoring social situations. As well, a modest relationship was observed between hypo-arousal to negative faces and problems with social monitoring. Finally, hypo-arousal in the anticipation of risk during the card game related to problems in empathy. In summary, these data are consistent with the view that alterations in the ability to perceive emotional expressions in the face and the disruption in arousal to personally-relevant information may be accounting for some of the difficulties in social adaptation often observed in persons who have sustained a TBI. Furthermore, these data provide modest support for Damasio's Somatic Marker Theory in that physiological reactivity to socially-relevant information has some value in predicting social function. Therefore, the assessment of TBI persons, particularly those with adaptive behavioural problems, should be expanded to determine whether alterations in perception and reactivity to socially-relevant stimuli have occurred. When this is the case, rehabilitative strategies aimed more specifically at these difficulties should be considered.

Carbohydrate refeeding rapidly reverses the adaptive upregulation of human skeletal muscle pyruvate dehydrogenase kinase following a high fat diet

The time course for the reversal of the adaptive increase in pyruvate dehydrogenase kinase (PDK) activity following a 6d high fat diet (HP: 4.2 ± 0.2 % carbohydrate; 75.6 ± 0.4 % fat; 19.5 ± 0.8 % protein) was investigated in human skeletal muscle (vastus lateralis). HF feeding increased PDK activity by 44% (from 0.081 ± 0.025 min"' to 0.247 ± 0.025 mm\p < 0.05). Following carbohydrate re-feeding, (88% carbohydrate; 5% fat; 7% protein), PDK activity had returned to baseline (0.111 ± 0.014 min"') within 3h of re-feeding. The active fraction of pyruvate dehydrognease (PDHa) was depressed following 6d of the HF diet (from 0.89 ± 0.21 mmol/min/kg WW to 0.32 ± 0.05 mmol/min/kg ww,p <0.05) and increased to pre-HF levels by 45 min of post re-feeding (0.74 ±0.19 mmol/min/kg ww) and remained elevated for 3h. Western blotting analysis of the PDK isoforms, PDK4 and PDK2, revealed a 31% increase in PDK4 protein content following the HF diet, with no change in PDK2 protein. This adaptive increase in PDK4 protein content was reversed with carbohydrate re-feeding. It was concluded that the adaptive up-regulation in PDK activity and PDK4 protein content was fiilly reversed by 3h following carbohydrate re-feeding.

The practical application of a meta-analysis of deinstitutionalization : adaptive behaviour outcomes and the piloting of a transitional questionnaire for adults with intellectual disabilities

This investigation examined the effects of de institutionalization on the adaptive behaviour and adjustment of adults with intellectual disabilities (ID). In study 1, a meta-analysis was conducted with 23 studies on deinstitutionalization adaptive behaviour outcomes. Deinstitutionalization was associated with modest improvements in adaptive behaviour however outcomes varied across adaptive behaviour domains and other substantive variables. Clinical and service implications of these results were explicated. Noting the trends from the meta-analysis, study 2 used this information in refining and piloting an Agency Transition Survey used to evaluate community transitions for persons with ID. Information derived from the survey was found to be valuable and adequate for the effective evaluation of transitional success. Potential applications of the survey and meta-analysis results were illustrated.

The Salmon Algorithm - A New Population Based Search Metaheuristic

This thesis introduces the Salmon Algorithm, a search meta-heuristic which can be used for a variety of combinatorial optimization problems. This algorithm is loosely based on the path finding behaviour of salmon swimming upstream to spawn. There are a number of tunable parameters in the algorithm, so experiments were conducted to find the optimum parameter settings for different search spaces. The algorithm was tested on one instance of the Traveling Salesman Problem and found to have superior performance to an Ant Colony Algorithm and a Genetic Algorithm. It was then tested on three coding theory problems - optimal edit codes, optimal Hamming distance codes, and optimal covering codes. The algorithm produced improvements on the best known values for five of six of the test cases using edit codes. It matched the best known results on four out of seven of the Hamming codes as well as three out of three of the covering codes. The results suggest the Salmon Algorithm is competitive with established guided random search techniques, and may be superior in some search spaces.

A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.

Deinstitutionalization and its Effects on Social Inclusion, Choice-Making, Adaptive and Maladaptive Behavior for Individuals with Intellectual Disabilities: Case Studies Analysis

In the past three decades institutions for persons with intellectual disabilities (ID) have been downsizing and closing in Ontario, Canada. This trend is reflective of the changes that have occurred in society. As of March 2009 the last institution operated by the Ontario government for persons with ID closed, placing the remaining approximately 1000 persons into the community. The current study was an analysis of part of one study in a four-study research project, called the Facilities Initiative Study, to explore the impact of the closures on the lives of individuals who have been reintegrated into community settings. The goal of the current case study analysis was to describe the impact of changes in social inclusion, choice-making/autonomy, and adaptive/maladaptive functioning of four individuals prior to and following transition to the community. The results suggested that, in most cases, community integration was related to more social inclusion opportunities and autonomy in choice-making, a wider range of adaptive behaviors and fewer maladaptive behaviors. In some cases, the evidence suggested that some of these indices of quality of life were not improving. Overall, the study found that the differences observed were unique to each of the individuals who participated in the case study analysis. Some generalized themes were generated that can be applied to future deinstitutionalization endeavors.

Assessing the adaptive capacity of the Ontario wine industry to climate change: A case study

The global wine industry is experiencing the impacts of climate change. Canada’s major wine sector, the Ontario Wine Industry (OWI) is no exception to this trend. Warmer winter and summer temperatures are affecting wine production. The industry needs to adapt to these challenges, but their capacity for this is unclear. To date, only a limited number of studies exist regarding the adaptive capacity of the wine industry to climate change. Accordingly, this study developed an adaptive capacity assessment framework for the wine industry. The OWI became the case study for the implementation of the assessment framework. Data was obtained by means of a questionnaire sent to grape growers, winemakers and supporting institutions in Ontario. The results indicated the OWI has adaptive capacity capabilities in financial, institutional, political, technological, perceptions, knowledge, diversity and social capital resources areas. Based on the OWI case study, this framework provides an effective means of assessing regional wine industries’ capacity to adapt to climate change.

New Contig Creation Algorithm for the de novo DNA Assembly Problem

DNA assembly is among the most fundamental and difficult problems in bioinformatics. Near optimal assembly solutions are available for bacterial and small genomes, however assembling large and complex genomes especially the human genome using Next-Generation-Sequencing (NGS) technologies is shown to be very difficult because of the highly repetitive and complex nature of the human genome, short read lengths, uneven data coverage and tools that are not specifically built for human genomes. Moreover, many algorithms are not even scalable to human genome datasets containing hundreds of millions of short reads. The DNA assembly problem is usually divided into several subproblems including DNA data error detection and correction, contig creation, scaffolding and contigs orientation; each can be seen as a distinct research area. This thesis specifically focuses on creating contigs from the short reads and combining them with outputs from other tools in order to obtain better results. Three different assemblers including SOAPdenovo [Li09], Velvet [ZB08] and Meraculous [CHS+11] are selected for comparative purposes in this thesis. Obtained results show that this thesis’ work produces comparable results to other assemblers and combining our contigs to outputs from other tools, produces the best results outperforming all other investigated assemblers.

A Study of Ordered Gene Problems Featuring DNA Error Correction and DNA Fragment Assembly with a Variety of Heuristics, Genetic Algorithm Variations, and Dynamic Representations

Ordered gene problems are a very common classification of optimization problems. Because of their popularity countless algorithms have been developed in an attempt to find high quality solutions to the problems. It is also common to see many different types of problems reduced to ordered gene style problems as there are many popular heuristics and metaheuristics for them due to their popularity. Multiple ordered gene problems are studied, namely, the travelling salesman problem, bin packing problem, and graph colouring problem. In addition, two bioinformatics problems not traditionally seen as ordered gene problems are studied: DNA error correction and DNA fragment assembly. These problems are studied with multiple variations and combinations of heuristics and metaheuristics with two distinct types or representations. The majority of the algorithms are built around the Recentering- Restarting Genetic Algorithm. The algorithm variations were successful on all problems studied, and particularly for the two bioinformatics problems. For DNA Error Correction multiple cases were found with 100% of the codes being corrected. The algorithm variations were also able to beat all other state-of-the-art DNA Fragment Assemblers on 13 out of 16 benchmark problem instances.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Fit For Action: A Comparative Case Study of the Implementation of an Adaptive Fitness and Conditioning Program for Moderate Functioning Teens and Transition Age Youth with ASD

The purpose of my research was to develop and refine pedagogic approaches, and establish fitness baselines to adapt fitness and conditioning programs for Moderate-functioning ASD individuals. I conducted a seven-week study with two teens and two trainers. The trainers implemented individualized fitness and conditioning programs that I developed. I conducted pre and post fitness baselines for each teen, a pre and post study interview with the trainers, and recorded semi-structured observations during each session. I used multi-level, within-case and across case analyses, working inductively and deductively. My findings indicated that fundamental movement concepts can be used to establish fitness baselines and develop individualized fitness programs. I tracked and evaluated progressions and improvements using conventional measurements applied to unconventional movements. This process contributed to understanding and making relevant modifications to activities as effective pedagogic strategies for my trainers. Further research should investigate fitness and conditioning programs with lower functioning ASD individuals.

Using PageRank Algorithm in Analyzing Dictionary Graphs and PageRank in Dynamic Graphs

In this thesis we are going to analyze the dictionary graphs and some other kinds of graphs using the PagerRank algorithm. We calculated the correlation between the degree and PageRank of all nodes for a graph obtained from Merriam-Webster dictionary, a French dictionary and WordNet hypernym and synonym dictionaries. Our conclusion was that PageRank can be a good tool to compare the quality of dictionaries. We studied some artificial social and random graphs. We found that when we omitted some random nodes from each of the graphs, we have not noticed any significant changes in the ranking of the nodes according to their PageRank. We also discovered that some social graphs selected for our study were less resistant to the changes of PageRank.