Radiomics treats images as quantitative data and promises to improve cancer prediction in radiology and therapy response assessment in radiation oncology. However, there are a number of fundamental problems that need to be solved in order to potentially apply radiomic features in clinic. The first basic step in computed tomography (CT) radiomic analysis is the acquisition of images using selectable image acquisition and reconstruction parameters. Radiomic features have shown large variability due to variation of these parameters. Therefore, it is important to develop methods to address these variability issues in radiomic features due to each CT parameter. To this end, texture phantoms provide a stable geometry and Hounsfield Units (HU) to characterize the radiomic features with respect to image acquisition and reconstruction parameters. In this project, normalization methods were developed to address the variability issues in CT Radiomics using texture phantoms. In the first part of this project, variability in radiomic features due to voxel size variation was addressed. A voxel size resampling method is presented as a preprocessing step for imaging data acquired with variable voxel sizes. After resampling, variability due to variable voxel size in 42 radiomic features was reduced significantly. Voxel size normalization is presented to address the intrinsic dependence of some key radiomic features. After normalization, 10 features became robust as a function of voxel size. Some of these features were identified as predictive biomarkers in diagnostic imaging or useful in response assessment in radiation therapy. However, these key features were found to be intrinsically dependent on voxel size (which also implies dependence on lesion volume). The normalization factors are also developed to address the intrinsic dependence of texture features on the number of gray levels. After normalization, the variability due to gray levels in 17 texture features was reduced significantly. In the second part of the project, voxel size and gray level (GL) normalizations developed based on phantom studies, were tested on the actual lung cancer tumors. Eighteen patients with non-small cell lung cancer of varying tumor volumes were studied and compared with phantom scans acquired on 8 different CT scanners. Eight out of 10 features showed high (Rs > 0.9) and low (Rs < 0.5) Spearman rank correlations with voxel size before and after normalizations, respectively. Likewise, texture features were unstable (ICC < 0.6) and highly stable (ICC > 0.9) before and after gray level normalizations, respectively. This work showed that voxel size and GL normalizations derived from texture phantom also apply to lung cancer tumors. This work highlights the importance and utility of investigating the robustness of CT radiomic features using CT texture phantoms. Another contribution of this work is to develop correction factors to address the variability issues in radiomic features due to reconstruction kernels. Reconstruction kernels and tube current contribute to noise texture in CT. Most of texture features were sensitive to correlated noise texture due to reconstruction kernels. In this work, noise power spectra (NPS) was measured on 5 CT scanners using standard ACR phantom to quantify the correlated noise texture. The variability in texture features due to different kernels was reduced by applying the NPS peak frequency and the region of interest (ROI) maximum intensity as correction factors. Most texture features were radiation dose independent but were strongly kernel dependent, which is demonstrated by a significant shift in NPS peak frequency among kernels. Percent improvements in robustness of 19 features were in the range of 30% to 78% after corrections. In conclusion, most texture features are sensitive to imaging parameters such as reconstruction kernels, reconstruction Field of View (FOV), and slice thickness. All reconstruction parameters contribute to inherent noise in CT images. The problem can be partly solved by quantifying noise texture in CT radiomics using a texture phantom and an ACR phantom. Texture phantoms should be a pre-requisite to patient studies as they provide stable geometry and HU distribution to characterize the radiomic features and provide ground truths for multi-institutional validation studies.

The question of how shape is represented is of central interest to understanding visual processing in cortex. While tuning properties of the cells in early part of the ventral visual stream, thought to be responsible for object recognition in the primate, are comparatively well understood, several different theories have been proposed regarding tuning in higher visual areas, such as V4. We used the model of object recognition in cortex presented by Riesenhuber and Poggio (1999), where more complex shape tuning in higher layers is the result of combining afferent inputs tuned to simpler features, and compared the tuning properties of model units in intermediate layers to those of V4 neurons from the literature. In particular, we investigated the issue of shape representation in visual area V1 and V4 using oriented bars and various types of gratings (polar, hyperbolic, and Cartesian), as used in several physiology experiments. Our computational model was able to reproduce several physiological findings, such as the broadening distribution of the orientation bandwidths and the emergence of a bias toward non-Cartesian stimuli. Interestingly, the simulation results suggest that some V4 neurons receive input from afferents with spatially separated receptive fields, leading to experimentally testable predictions. However, the simulations also show that the stimulus set of Cartesian and non-Cartesian gratings is not sufficiently complex to probe shape tuning in higher areas, necessitating the use of more complex stimulus sets.

This research investigates and proposes methods to be used for the automation of the conceptual design phases of variational mechanisms. It employs the concept of feature-based modeling approaches. A method is proposed for integrating the dimensional synthesis, mechanical design and CAD generation phases with minimal designer intervention. Extended feature definitions are used in this research to create a smooth data transfer platform between different engineering tools and applications. This paper also introduces another method by which a set of dimensional data collected from a family of existing products is used to predict possible solutions for a new design. This method, based on artificial neural networks for training and solution generation, is used with optimization algorithms for the dimensional synthesis of mechanisms. An excavator arm mechanism is used as a case study to demonstrate these methods. The design of this mechanism is carried out based on its digging mode configurations. / Engineering Design

This study investigated computer users’ help-seeking behaviors within the context of accomplishing a novel and challenging computer task. In addition, this study examined how different help-seeking behavioral variables relate to both personal factors and outcome measures in an exploratory manner. Finally, a structural model examined the effect of personal factors on task performance through the mediating function of help-seeking effectiveness. A total of 67 undergraduate students participated in the study. Participants were asked to perform a challenging task in Microsoft Word. The usability software MORAE was used to record the interactions between participants and computer systems. Participants had access to five help sources: “F1 help,” “reference book,” “the Web,” “video tutorial,” and “lab assistant,” which differ in media type (electronic vs. non-electronic) and interactivity levels (high vs. low). This study found that participants showed a wide range of help-seeking behaviors. Some participants were more active in seeking help than others. Participants also engaged in different help-seeking patterns when using different help sources. A dominant help-seeking strategy was to stay with the same source used in the previous help-seeking episode. Help-seeking behavior affected task performances, but personal factors had no significant effect on help seeking or task performances. Based on the findings, the research value of this study, its practical implications, its limitations, and future research directions are discussed.

Individuals with borderline personality disorder (BPD) experience severe and pervasive disturbances in the development of attachment relationships, identity, and emotion regulation. Given these deficits, mothers diagnosed with BPD are likely to experience significant difficulties in parenting their children. The present study examined the effect of maternal BPD and borderline personality features on emotional availability in interactions between mothers with BPD and their 4- to 7-year-old children. In a low socioeconomic status (SES) sample of n = 35 children of mothers diagnosed with BPD and n = 35 normative comparisons, groups were compared on maternal and child emotional availability, and self-reported maternal borderline personality features were assessed across the sample as a whole. No significant differences in emotional availability were found between groups. Across the sample as whole, however, maternal borderline personality features of affective instability, identity disturbance, negative relationships, and self-harm were significantly correlated with maternal intrusiveness and maternal hostility. Maternal borderline personality features of affective instability and negative relationships were significantly associated with maternal sensitivity, child responsiveness, and child involvement. Results are discussed in terms of putative precursors to BPD and preventive interventions.

Introduction: Research has shown that family firms play an important role in modern economies and that they maintain special characteristics and features in comparison to non-family businesses. Furthermore, it is evident in literature and practice that internationalization is a crucial process and strategy in the present global business environment. Problem: These findings however, have not led to a family business internationalization strategy. Only some studies have been conducted concerning the influence of the special features of family firms on the process of internationalization. This means that there is a gap between theory and practice. Purpose: To increase the academic understanding of the phenomenon internationalization of family businesses, through the use of both theoretical and empirical findings. Research questions: This study attempted to fulfill the purpose by providing answers to several research questions. The main research questions are: What is the current state of knowledge about internationalization, family business features and previous research in internationalization of family firms? How do the family business features theoretically influence the internationalization process? How do the family business features empirically influence the internationalization process? What are the theoretical contributions and practical managerial implications of these findings? Method: A solid literature research has been conducted in order to determine the theoretical influences of family business features on internationalization. The empirical testing of the expectations was conducted through a qualitative approach by taking personal interviews at eight companies, four in The Netherlands and four in Sweden, and studying secondary documentation. Findings: The study shows that it is difficult to decisively determine the either facilitating or constraining influence of family business features on internationalization. The results show that the 23 features which have been studied in the sample are facilitating or constraining under certain conditions (see table 5, p. 108). This implies that managers, researchers and consultants will be required to study these conditions first in order to determine the facilitating or constraining effects in a company. In addition, a model has been constructed illustrating the empirical findings concerning the internationalization process (see figure 8, p.112). Finally, concerning internationalization theories, it is determined that family businesses tend to use the Network approach in starting their internationalization process, the Stage approaches in further developing the international operations and support their process through the Resource-based view and the Knowledge-based view.

This thesis examined the factors correlated with rapid and benign progression of disease in a group of 1452 Parkinsons disease (PD) patients. The data were collected in a movement disorders clinic at the Royal University Hospital, University of Saskatchewan run by Dr. Alex Rajput and Dr. Ali Rajput. This data is a clinical dataset of PD patients collected from 1970 through to February, 2005. This was a retrospective cases-only study, with anticipated analytical follow-up if any correlations were detected between progression type of PD and the many independent variables available in the dataset. <p>Rapid progression was defined as those subjects who reached Hoehn and Yahr stage 3 within three years or H&Y stage 4 or 5 within five years. Subjects who remained in Hoehn and Yahr stage 1 or 2, ten years after onset of disease, were defined as having benign progression. The study analyzed demographic and clinical findings at first visit to this clinic associated with rapid and benign progression of PD. <p> Analysis revealed that, at first clinic visit, benign progression was positively associated with disease duration (OR=1.41; 95% CI 1.27, 1.57), male sex (OR=3.23; 95% CI 1.70, 6.16), and current smoking habit (OR=2.33; 95% CI 0.67, 8.11). Benign progression was negatively associated with older age of onset (OR=0.36; 95% CI 0.25, 0.50), past history of smoking (OR=0.46; 95% CI 0.24, 0.89), current or past use of levodopa (OR=0.45; 95% CI 0.21, 0.98), and mild to severe rigidity (OR=0.43; 95% CI 0.23, 0.80). <p>Analysis also revealed that, at first clinic visit, rapid progression was positively associated with older age of onset (OR=2.45; 95% CI 1.80, 3.33) and mild to severe rigidity (OR=1.73; 95% CI 1.02, 2.94). Rapid progression was negatively associated with disease duration (OR=0.52; 95% CI 0.44, 0.62), male sex (OR=0.58; CI 0.35, 0.95), and mild to severe resting tremor (OR=0.47; CI 0.28, 0.77). <p>The results of this study indicate that age of onset, disease duration, male sex, and rigidity are good potential predictors of disease progression in PD because they have opposite associations with rapid and benign progression. History of levodopa use was negatively associated with benign progression and as such may be good indicator of non-benign progression. Although previous studies found no predictive value for smoking history, the current study reported a unique association between smoking history and benign progression. Past smoking history was negatively associated with benign progression. While there was a positive association with current smoking history, the result was not statistically significant. Resting tremor was negatively associated with rapid progression and as such may be a good indicator of non-rapid progression. <p> Disease characteristics collected at first clinic visit are useful in predicting the course of progression of PD. With more rapid progression of PD closer and more frequent follow-up of patients may be necessary.

Near-infrared spectroscopy (NIRS) in an imaging technique that has gained much attention in brain-computer interfaces (BCIs). Previous NIRS-BCI studies have primarily employed temporal features, derived from the time course of hemodynamic activity, despite potential value contained in the spatial attributes of a response. In an initial offline study, we investigated the value of using joint spatial-temporal pattern classification with dynamic NIR topograms to differentiate intentional cortical activation from rest. With the inclusion of spatiotemporal features, we demonstrated a significant increase in achievable classification accuracies from those obtained using temporal features alone (p < 10-4). In a second study, we evaluated the feasibility of implementing joint spatial-temporal pattern classification in an online system. We developed an online system-paced NIRS-BCI, and were able to differentiate two cortical states with high accuracy (77.4±10.5%). Collectively, these findings demonstrate the value of including spatiotemporal features in the classification of functional NIRS data for BCI applications.

Today’s   market   is   subjected   to   numerous   changes   due   to   the   need   of   continuous improvement  of  different  commercial  brands  in  order  to  survive  against  competitors.  This competition  drives  the  evolution  of  industrial  processes,  to  satisfy  the  high  customers’ requirements. It means that factors such as flexibility, adaptability and agility are crucial for the  successful  development  of  industries,  which  experience  some  degrees  of  uncertainty due  to  machine  breakdowns,  delays  and  market  fluctuations  among  others.  The  current trend  in  manufacturing  industries  consists  in  the  implementation  of  distributed  control systems (DCS), substituting the earlier programmable logic controllers (PLC) systems where a main  processor  operated  as  the  central  unit  of  the  system.  To  this  end,  the  application  of function  blocks  (FB)  compliant  with  the  IEC  61499  standard  represents  an  innovative technique  for  dealing  with  the  design  and  programming  of  DCSs.  These  FBs  enable  the creation  of  event-driven  networks  governed  by  embedded  algorithms  that  can  be  used  to enhance  the  flexibility  and  portability  of  industrial  job-shops  based  on  a  distributed architecture.  Job-shop  floors  represent  a  principal  concept  in  manufacturing  industries.  This  project  is focused on the integration of a computer numerically controlled (CNC) machine and a gantry robot  which  must  be  coordinated  and  cooperate  for  the  achievement  of  an  industrial machining  and  assembly  process.  It  implies  the  design  of  a  PLC-managed  distributed  cell using  nxtControl  software.  This  software  facilitates  the  construction  of  FBs-networks  to control both machines and enables the communication process via service interface function blocks (SI-FB). Likewise, the whole process will be monitored using an interface also created within nxtControl which will allow the operator to decide the batch and characteristics of the production.  This project is also intended to set the basis for the understanding of the FB concept defined in  IEC  61499  which  moves  away  from  earlier  scan-based  systems  to  event-driven  models, aiming to contribute to the development of future research in the function blocks area.

The goal of this thesis is to develop better practical methods for detecting common object classes in real world images. We present a family of object detectors that combine Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) features with efficient Latent SVM classifiers and effective dimensionality reduction and sparsification schemes to give state-of-the-art performance on several important datasets including PASCAL VOC2006 and VOC2007, INRIA Person and ETHZ. The three main contributions are as follows. Firstly, we pioneer the use of Local Ternary Pattern features for object detection, showing that LTP gives better overall performance than HOG and LBP, because it captures both rich local texture and object shape information while being resistant to variations in lighting conditions. It thus works well both for classes that are recognized mainly by their structure and ones that are recognized mainly by their textures. We also show that HOG, LBP and LTP complement one another, so that an extended feature set that incorporates all three of them gives further improvements in performance. Secondly, in order to tackle the speed and memory usage problems associated with high-dimensional modern feature sets, we propose two effective dimensionality reduction techniques. The first, feature projection using Partial Least Squares, allows detectors to be trained more rapidly with negligible loss of accuracy and no loss of run time speed for linear detectors. The second, feature selection using SVM weight truncation, allows active feature sets to be reduced in size by almost an order of magnitude with little or no loss, and often a small gain, in detector accuracy. Despite its simplicity, this feature selection scheme outperforms all of the other sparsity enforcing methods that we have tested. Lastly, we describe work in progress on Local Quantized Patterns (LQP), a generalized form of local pattern features that uses lookup table based vector quantization to provide local pattern style pixel neighbourhood codings that have the speed of LBP/LTP and some of the flexibility and power of traditional visual word representations. Our experiments show that LQP outperforms all of the other feature sets tested including HOG, LBP and LTP.