Design and development of a one-degree-of-freedom force-reflecting manual controller prototype for teleoperation

The present research is carried out from the viewpoint of primarily space applications where human lives may be in danger if they are to work under these conditions. This work proposes to develop a one-degree-of-freedom (1-DOF) force-reflecting manual controller (FRMC) prototype for teleoperation, and address the effects of time delays commonly found in space applications where the control is accomplished via the earth-based control stations. To test the FRMC, a mobile robot (PPRK) and a slider-bar were developed and integrated to the 1-DOF FRMC. The software developed in Visual Basic is able to telecontrol any platform that uses an SV203 controller through the internet and it allows the remote system to send feedback information which may be in the form of visual or force signals. Time delay experiments were conducted on the platform and the effects of time delay on the FRMC system operation have been studied and delineated.

Design and implementation of a multisite data repository for pediatric epilepsy

This dissertation established a software-hardware integrated design for a multisite data repository in pediatric epilepsy. A total of 16 institutions formed a consortium for this web-based application. This innovative fully operational web application allows users to upload and retrieve information through a unique human-computer graphical interface that is remotely accessible to all users of the consortium. A solution based on a Linux platform with My-SQL and Personal Home Page scripts (PHP) has been selected. Research was conducted to evaluate mechanisms to electronically transfer diverse datasets from different hospitals and collect the clinical data in concert with their related functional magnetic resonance imaging (fMRI). What was unique in the approach considered is that all pertinent clinical information about patients is synthesized with input from clinical experts into 4 different forms, which were: Clinical, fMRI scoring, Image information, and Neuropsychological data entry forms. A first contribution of this dissertation was in proposing an integrated processing platform that was site and scanner independent in order to uniformly process the varied fMRI datasets and to generate comparative brain activation patterns. The data collection from the consortium complied with the IRB requirements and provides all the safeguards for security and confidentiality requirements. An 1-MR1-based software library was used to perform data processing and statistical analysis to obtain the brain activation maps. Lateralization Index (LI) of healthy control (HC) subjects in contrast to localization-related epilepsy (LRE) subjects were evaluated. Over 110 activation maps were generated, and their respective LIs were computed yielding the following groups: (a) strong right lateralization: (HC=0%, LRE=18%), (b) right lateralization: (HC=2%, LRE=10%), (c) bilateral: (HC=20%, LRE=15%), (d) left lateralization: (HC=42%, LRE=26%), e) strong left lateralization: (HC=36%, LRE=31%). Moreover, nonlinear-multidimensional decision functions were used to seek an optimal separation between typical and atypical brain activations on the basis of the demographics as well as the extent and intensity of these brain activations. The intent was not to seek the highest output measures given the inherent overlap of the data, but rather to assess which of the many dimensions were critical in the overall assessment of typical and atypical language activations with the freedom to select any number of dimensions and impose any degree of complexity in the nonlinearity of the decision space.

Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Comprehensive process maps for synthesizing high density aluminum oxide-carbon nanotube coatings by plasma spraying for improved mechanical and wear properties

Plasma sprayed aluminum oxide ceramic coating is widely used due to its outstanding wear, corrosion, and thermal shock resistance. But porosity is the integral feature in the plasma sprayed coating which exponentially degrades its properties. In this study, process maps were developed to obtain Al2O3-CNT composite coatings with the highest density (i.e. lowest porosity) and improved mechanical and wear properties. Process map is defined as a set of relationships that correlates large number of plasma processing parameters to the coating properties. Carbon nanotubes (CNTs) were added as reinforcement to Al2O 3 coating to improve the fracture toughness and wear resistance. Two novel powder processing approaches viz spray drying and chemical vapor growth were adopted to disperse CNTs in Al2O3 powder. The degree of CNT dispersion via chemical vapor deposition (CVD) was superior to spray drying but CVD could not synthesize powder in large amount. Hence optimization of plasma processing parameters and process map development was limited to spray dried Al2O3 powder containing 0, 4 and 8 wt. % CNTs. An empirical model using Pareto diagram was developed to link plasma processing parameters with the porosity of coating. Splat morphology as a function of plasma processing parameter was also studied to understand its effect on mechanical properties. Addition of a mere 1.5 wt. % CNTs via CVD technique showed ∼27% and ∼24% increase in the elastic modulus and fracture toughness respectively. Improved toughness was attributed to combined effect of lower porosity and uniform dispersion of CNTs which promoted the toughening by CNT bridging, crack deflection and strong CNT/Al2O3 interface. Al2O 3-8 wt. % CNT coating synthesized using spray dried powder showed 73% improvement in the fracture toughness when porosity reduced from 4.7% to 3.0%. Wear resistance of all coatings at room and elevated temperatures (573 K, 873 K) showed improvement with CNT addition and decreased porosity. Such behavior was due to improved mechanical properties, protective film formation due to tribochemical reaction, and CNT bridging between the splats. Finally, process maps correlating porosity content, CNT content, mechanical properties, and wear properties were developed.

Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.