Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^

Improving resource management in virtualized data centers using application performance models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity.^ We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. ^ This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.^

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.

A comparative analysis of self-determined evaluations of work performance and support needs by adults with mental retardation in supported work and in sheltered workshop environments

The purpose of this study was to determine if there was a difference in the self-determined evaluations of work performance and support needs by adults with mental retardation in supported employment and in sheltered workshop environments. The instrument, Job Observation and Behavior Scale: Opportunity for Self-Determination (JOBS: OSD; Brady, Rosenberg, & Frain, 2006), was administered to 38 adults with mental retardation from sheltered workshops and 32 adults with mental retardation from supported employment environments. Cross-tabulations with Chi-square tests and independent samples t-tests were conducted to evaluate differences between the two groups, sheltered workshop and supported work. Two Multivariate Analyses of Variance (MANOVAs) were conducted to determine the effect of work environment on Quality of Performance (QP) and Types of Support (TS) test scores and their subscales. ^ This study found that there were significant differences between the groups on the QP Behavior and Job Duties subscales. The sheltered workshop group perceived themselves as performing significantly better on job duties than the supported work group. Conversely, the supported work group perceived themselves to have better behavior than the sheltered workshop group. However, there were no significant differences between groups in their perception of support needs for the three subscales. ^ The findings imply that work environment affects the self-determined evaluations of work performance by adults with mental retardation. Recommendations for further study include (a) detailing the characteristics of supported work and sheltered workshops that support and/or discourage self-determined behaviors, (b) exploring the behavior of adults with mental retardation in sheltered workshops and supported work environments, and (c) analysis of the support needs for and understanding of them by adults with mental retardation in sheltered workshops and in supported work environments. ^

A digital signal processing approach for affective sensing of a computer user through pupil diameter monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma

In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of astrocytoma. Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I–IV), and ‘key genes’ within each grade were identified. Genes found to be most influential to development of the highest grade of astrocytoma, Glioblastoma multiforme were: COL4A1, EGFR, BTF3, MPP2, RAB31, CDK4, CD99, ANXA2, TOP2A, and SERBP1. All of these genes were up-regulated, except MPP2 (down regulated). These 10 genes were able to predict tumor status with 96–100% confidence when using logistic regression, cross validation, and the support vector machine analysis. Markov genes interact with NFkβ, ERK, MAPK, VEGF, growth hormone and collagen to produce a network whose top biological functions are cancer, neurological disease, and cellular movement. Three of the 10 genes - EGFR, COL4A1, and CDK4, in particular, seemed to be potential ‘hubs of activity’. Modified expression of these 10 Markov Blanket genes increases lifetime risk of developing glioblastoma compared to the normal population. The glioblastoma risk estimates were dramatically increased with joint effects of 4 or more than 4 Markov Blanket genes. Joint interaction effects of 4, 5, 6, 7, 8, 9 or 10 Markov Blanket genes produced 9, 13, 20.9, 26.7, 52.8, 53.2, 78.1 or 85.9%, respectively, increase in lifetime risk of developing glioblastoma compared to normal population. In summary, it appears that modified expression of several ‘key genes’ may be required for the development of glioblastoma. Further studies are needed to validate these ‘key genes’ as useful tools for early detection and novel therapeutic options for these tumors.

Romantic Relationships and Adjustment in Early Adulthood: The Role of Cross-sex Experience in the Context of Social Norms and Support

Attachment and interpersonal theory suggest a sequential pattern of relationships beginning in the earliest stage of development and progressing to social and eventually romantic relationships. Theoretically, cross-sex experiences have an important role in the progression of interpersonal relationships. Despite the prevalence of these theories about the nature of romantic relationship development, the linkage of cross-sex experience (CSE) to romantic relationships has not been established. Indeed, it is an intuitive assumption, especially within Western society and these theories do not consider socio-cultural factors that may influence CSE and relationship satisfaction. This study addresses the varying contextual factors that may contribute to relationship satisfaction and adjustment, aside from CSE, and is divided into two parts. Study 1, addresses CSE, relationship satisfaction, and adjustment in a unique population, ultra-Orthodox Jews. Among this population, social or romantic CSE is limited and sexes are effectively segregated. Study 2, expanded the study to a larger sample of U.S. college students, to assess the linkage of CSE to romantic relationship satisfaction in a more typical Western population. It included social norm and support variables to address the contextual nature of relationship development and satisfaction. Results demonstrated clear differences in the relation between CSE and relationship satisfaction in the two samples. In the first sample CSE was unrelated to relationship satisfaction; nevertheless, relationship satisfaction was associated with adjustment as it is for more typical populations with greater CSE. These results suggested the importance of specifying how social norms and social support relate to CSE, relationship satisfaction and adjustment. The results from the second sample were consistent with the theoretical framework upon which the social/romantic literature is based. CSE was directly connected to relationship satisfaction. As anticipated, CSE, relationship satisfaction, and adjustment also varied as a function of social norms and support. These findings further validate the influence of socio-cultural factors on relationship satisfaction and adjustment. This study contributes to the romantic relationship literature and broadens our understanding of the complex nature of interpersonal and romantic relationships.

A Digital Signal Processing Approach for Affective Sensing of a Computer User through Pupil Diameter Monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

Romantic relationships and adjustment in early adulthood: The role of cross-sex experience in the context of social norms and support

Attachment and interpersonal theory suggest a sequential pattern of relationships beginning in the earliest stage of development and progressing to social and eventually romantic relationships. Theoretically, cross-sex experiences have an important role in the progression of interpersonal relationships. Despite the prevalence of these theories about the nature of romantic relationship development, the linkage of cross-sex experience (CSE) to romantic relationships has not been established. Indeed, it is an intuitive assumption, especially within Western society and these theories do not consider socio-cultural factors that may influence CSE and relationship satisfaction. This study addresses the varying contextual factors that may contribute to relationship satisfaction and adjustment, aside from CSE, and is divided into two parts. Study 1, addresses CSE, relationship satisfaction, and adjustment in a unique population, ultra-Orthodox Jews. Among this population, social or romantic CSE is limited and sexes are effectively segregated. Study 2, expanded the study to a larger sample of U.S. college students, to assess the linkage of CSE to romantic relationship satisfaction in a more typical Western population. It included social norm and support variables to address the contextual nature of relationship development and satisfaction. Results demonstrated clear differences in the relation between CSE and relationship satisfaction in the two samples. In the first sample CSE was unrelated to relationship satisfaction; nevertheless, relationship satisfaction was associated with adjustment as it is for more typical populations with greater CSE. These results suggested the importance of specifying how social norms and social support relate to CSE, relationship satisfaction and adjustment. The results from the second sample were consistent with the theoretical framework upon which the social/romantic literature is based. CSE was directly connected to relationship satisfaction. As anticipated, CSE, relationship satisfaction, and adjustment also varied as a function of social norms and support. These findings further validate the influence of socio-cultural factors on relationship satisfaction and adjustment. This study contributes to the romantic relationship literature and broadens our understanding of the complex nature of interpersonal and romantic relationships.^

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Multidimensional classification and diagnosis of leukemia in flow cytometry

This research is to establish new optimization methods for pattern recognition and classification of different white blood cells in actual patient data to enhance the process of diagnosis. Beckman-Coulter Corporation supplied flow cytometry data of numerous patients that are used as training sets to exploit the different physiological characteristics of the different samples provided. The methods of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used as promising pattern classification techniques to identify different white blood cell samples and provide information to medical doctors in the form of diagnostic references for the specific disease states, leukemia. The obtained results prove that when a neural network classifier is well configured and trained with cross-validation, it can perform better than support vector classifiers alone for this type of data. Furthermore, a new unsupervised learning algorithm---Density based Adaptive Window Clustering algorithm (DAWC) was designed to process large volumes of data for finding location of high data cluster in real-time. It reduces the computational load to ∼O(N) number of computations, and thus making the algorithm more attractive and faster than current hierarchical algorithms.

A large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling

With the exponential increasing demands and uses of GIS data visualization system, such as urban planning, environment and climate change monitoring, weather simulation, hydrographic gauge and so forth, the geospatial vector and raster data visualization research, application and technology has become prevalent. However, we observe that current web GIS techniques are merely suitable for static vector and raster data where no dynamic overlaying layers. While it is desirable to enable visual explorations of large-scale dynamic vector and raster geospatial data in a web environment, improving the performance between backend datasets and the vector and raster applications remains a challenging technical issue. This dissertation is to implement these challenging and unimplemented areas: how to provide a large-scale dynamic vector and raster data visualization service with dynamic overlaying layers accessible from various client devices through a standard web browser, and how to make the large-scale dynamic vector and raster data visualization service as rapid as the static one. To accomplish these, a large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling and a comprehensive performance improvement solution are proposed, designed and implemented. They include: the quadtree-based indexing and parallel map tiling, the Legend String, the vector data visualization with dynamic layers overlaying, the vector data time series visualization, the algorithm of vector data rendering, the algorithm of raster data re-projection, the algorithm for elimination of superfluous level of detail, the algorithm for vector data gridding and re-grouping and the cluster servers side vector and raster data caching.

Design Optimization of Modern Machine-drive Systems for Maximum Fault Tolerant and Optimal Operation

Modern electric machine drives, particularly three phase permanent magnet machine drive systems represent an indispensable part of high power density products. Such products include; hybrid electric vehicles, large propulsion systems, and automation products. Reliability and cost of these products are directly related to the reliability and cost of these systems. The compatibility of the electric machine and its drive system for optimal cost and operation has been a large challenge in industrial applications. The main objective of this dissertation is to find a design and control scheme for the best compromise between the reliability and optimality of the electric machine-drive system. The effort presented here is motivated by the need to find new techniques to connect the design and control of electric machines and drive systems. A highly accurate and computationally efficient modeling process was developed to monitor the magnetic, thermal, and electrical aspects of the electric machine in its operational environments. The modeling process was also utilized in the design process in form finite element based optimization process. It was also used in hardware in the loop finite element based optimization process. The modeling process was later employed in the design of a very accurate and highly efficient physics-based customized observers that are required for the fault diagnosis as well the sensorless rotor position estimation. Two test setups with different ratings and topologies were numerically and experimentally tested to verify the effectiveness of the proposed techniques. The modeling process was also employed in the real-time demagnetization control of the machine. Various real-time scenarios were successfully verified. It was shown that this process gives the potential to optimally redefine the assumptions in sizing the permanent magnets of the machine and DC bus voltage of the drive for the worst operating conditions. The mathematical development and stability criteria of the physics-based modeling of the machine, design optimization, and the physics-based fault diagnosis and the physics-based sensorless technique are described in detail. To investigate the performance of the developed design test-bed, software and hardware setups were constructed first. Several topologies of the permanent magnet machine were optimized inside the optimization test-bed. To investigate the performance of the developed sensorless control, a test-bed including a 0.25 (kW) surface mounted permanent magnet synchronous machine example was created. The verification of the proposed technique in a range from medium to very low speed, effectively show the intelligent design capability of the proposed system. Additionally, to investigate the performance of the developed fault diagnosis system, a test-bed including a 0.8 (kW) surface mounted permanent magnet synchronous machine example with trapezoidal back electromotive force was created. The results verify the use of the proposed technique under dynamic eccentricity, DC bus voltage variations, and harmonic loading condition make the system an ideal case for propulsion systems.

Aedes aegypti Pharate First Instar Aseasonal Quiescence Cues Anticipatory Plasticity with Implications for Urban Vector Ecology and Control

The eggs of the dengue fever vector Aedes aegypti possess the ability to undergo an extended quiescence period hosting a fully developed first instar larvae within its chorion. As a result of this life history stage, pharate larvae can withstand months of dormancy inside the egg where they depend on stored reserves of maternal origin. This adaptation known as pharate first instar quiescence, allows A. aegypti to cope with fluctuations in water availability. An examination of this fundamental adaptation has shown that there are trade-offs associated with it. Aedes aegypti mosquitoes are frequently associated with urban habitats that may contain metal pollution. My research has demonstrated that the duration of this quiescence and the extent of nutritional depletion associated with it affects the physiology and survival of larvae that hatch in a suboptimal habitat; nutrient reserves decrease during pharate first instar quiescence and alter subsequent larval and adult fitness. The duration of quiescence compromises metal tolerance physiology and is coupled to a decrease in metallothionein mRNA levels. My findings also indicate that even low levels of environmentally relevant larval metal stress alter the parameters that determine vector capacity. My research has also demonstrated that extended pharate first instar quiescence can elicit a plastic response resulting in an adult phenotype distinct from adults reared from short quiescence eggs. Extended pharate first instar quiescence affects the performance and reproductive fitness of the adult female mosquito as well as the nutritional status of its progeny via maternal effects in an adaptive manner, i.e., anticipatory phenotypic plasticity results as a consequence of the duration of pharate first instar quiescence and alternative phenotypes may exist for this mosquito with quiescence serving as a cue possibly signaling the environmental conditions that follow a dry period. M findings may explain, in part, A. aegypti’s success as a vector and its geographic distribution and have implications for its vector capacity and control.