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.

Efficient asynchronous scheduling algorithms for cost-effective buffered crossbar switches

Buffered crossbar switches have recently attracted considerable attention as the next generation of high speed interconnects. They are a special type of crossbar switches with an exclusive buffer at each crosspoint of the crossbar. They demonstrate unique advantages over traditional unbuffered crossbar switches, such as high throughput, low latency, and asynchronous packet scheduling. However, since crosspoint buffers are expensive on-chip memories, it is desired that each crosspoint has only a small buffer. This dissertation proposes a series of practical algorithms and techniques for efficient packet scheduling for buffered crossbar switches. To reduce the hardware cost of such switches and make them scalable, we considered partially buffered crossbars, whose crosspoint buffers can be of an arbitrarily small size. Firstly, we introduced a hybrid scheme called Packet-mode Asynchronous Scheduling Algorithm (PASA) to schedule best effort traffic. PASA combines the features of both distributed and centralized scheduling algorithms and can directly handle variable length packets without Segmentation And Reassembly (SAR). We showed by theoretical analysis that it achieves 100% throughput for any admissible traffic in a crossbar with a speedup of two. Moreover, outputs in PASA have a large probability to avoid the more time-consuming centralized scheduling process, and thus make fast scheduling decisions. Secondly, we proposed the Fair Asynchronous Segment Scheduling (FASS) algorithm to handle guaranteed performance traffic with explicit flow rates. FASS reduces the crosspoint buffer size by dividing packets into shorter segments before transmission. It also provides tight constant performance guarantees by emulating the ideal Generalized Processor Sharing (GPS) model. Furthermore, FASS requires no speedup for the crossbar, lowering the hardware cost and improving the switch capacity. Thirdly, we presented a bandwidth allocation scheme called Queue Length Proportional (QLP) to apply FASS to best effort traffic. QLP dynamically obtains a feasible bandwidth allocation matrix based on the queue length information, and thus assists the crossbar switch to be more work-conserving. The feasibility and stability of QLP were proved, no matter whether the traffic distribution is uniform or non-uniform. Hence, based on bandwidth allocation of QLP, FASS can also achieve 100% throughput for best effort traffic in a crossbar without speedup.

Design of an automated book reader as an assistive technology tool for blind persons

This dissertation introduces a novel automated book reader as an assistive technology tool for persons with blindness. The literature shows extensive work in the area of optical character recognition, but the current methodologies available for the automated reading of books or bound volumes remain inadequate and are severely constrained during document scanning or image acquisition processes. The goal of the book reader design is to automate and simplify the task of reading a book while providing a user-friendly environment with a realistic but affordable system design. This design responds to the main concerns of (a) providing a method of image acquisition that maintains the integrity of the source (b) overcoming optical character recognition errors created by inherent imaging issues such as curvature effects and barrel distortion, and (c) determining a suitable method for accurate recognition of characters that yields an interface with the ability to read from any open book with a high reading accuracy nearing 98%. This research endeavor focuses in its initial aim on the development of an assistive technology tool to help persons with blindness in the reading of books and other bound volumes. But its secondary and broader aim is to also find in this design the perfect platform for the digitization process of bound documentation in line with the mission of the Open Content Alliance (OCA), a nonprofit Alliance at making reading materials available in digital form. The theoretical perspective of this research relates to the mathematical developments that are made in order to resolve both the inherent distortions due to the properties of the camera lens and the anticipated distortions of the changing page curvature as one leafs through the book. This is evidenced by the significant increase of the recognition rate of characters and a high accuracy read-out through text to speech processing. This reasonably priced interface with its high performance results and its compatibility to any computer or laptop through universal serial bus connectors extends greatly the prospects for universal accessibility to documentation.

Optimum wireless power transmission for sensors embedded in concrete

Various nondestructive testing (NDT) technologies for construction and performance monitoring have been studied for decades. Recently, the rapid evolution of wireless sensor network (WSN) technologies has enabled the development of sensors that can be embedded in concrete to monitor the structural health of infrastructure. Such sensors can be buried inside concrete and they can collect and report valuable volumetric data related to the health of a structure during and/or after construction. Wireless embedded sensors monitoring system is also a promising solution for decreasing the high installation and maintenance cost of the conventional wire based monitoring systems. Wireless monitoring sensors need to operate for long time. However, sensor batteries have finite life-time. Therefore, in order to enable long operational life of wireless sensors, novel wireless powering methods, which can charge the sensors’ rechargeable batteries wirelessly, need to be developed. The optimization of RF wireless powering of sensors embedded in concrete is studied here. First, our analytical results focus on calculating the transmission loss and propagation loss of electromagnetic waves penetrating into plain concrete at different humidity conditions for various frequencies. This analysis specifically leads to the identification of an optimum frequency range within 20–80 MHz that is validated through full-wave electromagnetic simulations. Second, the effects of various reinforced bar configurations on the efficiency of wireless powering are investigated. Specifically, effects of the following factors are studied: rebar types, rebar period, rebar radius, depth inside concrete, and offset placement. This analysis leads to the identification of the 902–928 MHz ISM band as the optimum power transmission frequency range for sensors embedded in reinforced concrete, since antennas working in this band are less sensitive to the effects of varying humidity as well as rebar configurations. Finally, optimized rectennas are designed for receiving and/or harvesting power in order to charge the rechargeable batteries of the embedded sensors. Such optimized wireless powering systems exhibit significantly larger efficiencies than the efficiencies of conventional RF wireless powering systems for sensors embedded in plain or reinforced concrete.

Thermal imaging as a biometrics approach to facial signature authentication

This dissertation develops an image processing framework with unique feature extraction and similarity measurements for human face recognition in the thermal mid-wave infrared portion of the electromagnetic spectrum. The goals of this research is to design specialized algorithms that would extract facial vasculature information, create a thermal facial signature and identify the individual. The objective is to use such findings in support of a biometrics system for human identification with a high degree of accuracy and a high degree of reliability. This last assertion is due to the minimal to no risk for potential alteration of the intrinsic physiological characteristics seen through thermal infrared imaging. The proposed thermal facial signature recognition is fully integrated and consolidates the main and critical steps of feature extraction, registration, matching through similarity measures, and validation through testing our algorithm on a database, referred to as C-X1, provided by the Computer Vision Research Laboratory at the University of Notre Dame. Feature extraction was accomplished by first registering the infrared images to a reference image using the functional MRI of the Brain’s (FMRIB’s) Linear Image Registration Tool (FLIRT) modified to suit thermal infrared images. This was followed by segmentation of the facial region using an advanced localized contouring algorithm applied on anisotropically diffused thermal images. Thermal feature extraction from facial images was attained by performing morphological operations such as opening and top-hat segmentation to yield thermal signatures for each subject. Four thermal images taken over a period of six months were used to generate thermal signatures and a thermal template for each subject, the thermal template contains only the most prevalent and consistent features. Finally a similarity measure technique was used to match signatures to templates and the Principal Component Analysis (PCA) was used to validate the results of the matching process. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using an Euclidean-based similarity measure showed 88% accuracy in the case of skeletonized signatures and templates, we obtained 90% accuracy for anisotropically diffused signatures and templates. We also employed the Manhattan-based similarity measure and obtained an accuracy of 90.39% for skeletonized and diffused templates and signatures. It was found that an average 18.9% improvement in the similarity measure was obtained when using diffused templates. The Euclidean- and Manhattan-based similarity measure was also applied to skeletonized signatures and templates of 25 subjects in the C-X1 database. The highly accurate results obtained in the matching process along with the generalized design process clearly demonstrate the ability of the thermal infrared system to be used on other thermal imaging based systems and related databases. A novel user-initialization registration of thermal facial images has been successfully implemented. Furthermore, the novel approach at developing a thermal signature template using four images taken at various times ensured that unforeseen changes in the vasculature did not affect the biometric matching process as it relied on consistent thermal features.

Scheduling medical application workloads on virtualized computing systems

This dissertation presents and evaluates a methodology for scheduling medical application workloads in virtualized computing environments. Such environments are being widely adopted by providers of "cloud computing" services. In the context of provisioning resources for medical applications, such environments allow users to deploy applications on distributed computing resources while keeping their data secure. Furthermore, higher level services that further abstract the infrastructure-related issues can be built on top of such infrastructures. For example, a medical imaging service can allow medical professionals to process their data in the cloud, easing them from the burden of having to deploy and manage these resources themselves. In this work, we focus on issues related to scheduling scientific workloads on virtualized environments. We build upon the knowledge base of traditional parallel job scheduling to address the specific case of medical applications while harnessing the benefits afforded by virtualization technology. To this end, we provide the following contributions: (1) An in-depth analysis of the execution characteristics of the target applications when run in virtualized environments. (2) A performance prediction methodology applicable to the target environment. (3) A scheduling algorithm that harnesses application knowledge and virtualization-related benefits to provide strong scheduling performance and quality of service guarantees. In the process of addressing these pertinent issues for our target user base (i.e. medical professionals and researchers), we provide insight that benefits a large community of scientific application users in industry and academia. Our execution time prediction and scheduling methodologies are implemented and evaluated on a real system running popular scientific applications. We find that we are able to predict the execution time of a number of these applications with an average error of 15%. Our scheduling methodology, which is tested with medical image processing workloads, is compared to that of two baseline scheduling solutions and we find that it outperforms them in terms of both the number of jobs processed and resource utilization by 20–30%, without violating any deadlines. We conclude that our solution is a viable approach to supporting the computational needs of medical users, even if the cloud computing paradigm is not widely adopted in its current form.

VHITS: Vertical handoff initiation and target selection in a heterogeneous wireless network

Global connectivity, for anyone, at anyplace, at anytime, to provide high-speed, high-quality, and reliable communication channels for mobile devices, is now becoming a reality. The credit mainly goes to the recent technological advances in wireless communications comprised of a wide range of technologies, services, and applications to fulfill the particular needs of end-users in different deployment scenarios (Wi-Fi, WiMAX, and 3G/4G cellular systems). In such a heterogeneous wireless environment, one of the key ingredients to provide efficient ubiquitous computing with guaranteed quality and continuity of service is the design of intelligent handoff algorithms. Traditional single-metric handoff decision algorithms, such as Received Signal Strength (RSS) based, are not efficient and intelligent enough to minimize the number of unnecessary handoffs, decision delays, and call-dropping and/or blocking probabilities. This research presented a novel approach for the design and implementation of a multi-criteria vertical handoff algorithm for heterogeneous wireless networks. Several parallel Fuzzy Logic Controllers were utilized in combination with different types of ranking algorithms and metric weighting schemes to implement two major modules: the first module estimated the necessity of handoff, and the other module was developed to select the best network as the target of handoff. Simulations based on different traffic classes, utilizing various types of wireless networks were carried out by implementing a wireless test-bed inspired by the concept of Rudimentary Network Emulator (RUNE). Simulation results indicated that the proposed scheme provided better performance in terms of minimizing the unnecessary handoffs, call dropping, and call blocking and handoff blocking probabilities. When subjected to Conversational traffic and compared against the RSS-based reference algorithm, the proposed scheme, utilizing the FTOPSIS ranking algorithm, was able to reduce the average outage probability of MSs moving with high speeds by 17%, new call blocking probability by 22%, the handoff blocking probability by 16%, and the average handoff rate by 40%. The significant reduction in the resulted handoff rate provides MS with efficient power consumption, and more available battery life. These percentages indicated a higher probability of guaranteed session continuity and quality of the currently utilized service, resulting in higher user satisfaction levels.

Modeling, simulation, and characterization of space debris in low-Earth orbit

Every space launch increases the overall amount of space debris. Satellites have limited awareness of nearby objects that might pose a collision hazard. Astrometric, radiometric, and thermal models for the study of space debris in low-Earth orbit have been developed. This modeled approach proposes analysis methods that provide increased Local Area Awareness for satellites in low-Earth and geostationary orbit. Local Area Awareness is defined as the ability to detect, characterize, and extract useful information regarding resident space objects as they move through the space environment surrounding a spacecraft. The study of space debris is of critical importance to all space-faring nations. Characterization efforts are proposed using long-wave infrared sensors for space-based observations of debris objects in low-Earth orbit. Long-wave infrared sensors are commercially available and do not require solar illumination to be observed, as their received signal is temperature dependent. The characterization of debris objects through means of passive imaging techniques allows for further studies into the origination, specifications, and future trajectory of debris objects. Conclusions are made regarding the aforementioned thermal analysis as a function of debris orbit, geometry, orientation with respect to time, and material properties. Development of a thermal model permits the characterization of debris objects based upon their received long-wave infrared signals. Information regarding the material type, size, and tumble-rate of the observed debris objects are extracted. This investigation proposes the utilization of long-wave infrared radiometric models of typical debris to develop techniques for the detection and characterization of debris objects via signal analysis of unresolved imagery. Knowledge regarding the orbital type and semi-major axis of the observed debris object are extracted via astrometric analysis. This knowledge may aid in the constraint of the admissible region for the initial orbit determination process. The resultant orbital information is then fused with the radiometric characterization analysis enabling further characterization efforts of the observed debris object. This fused analysis, yielding orbital, material, and thermal properties, significantly increases a satellite's Local Area Awareness via an intimate understanding of the debris environment surrounding the spacecraft.

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Delay-sensitive service request scheduling for cloud computing

Cloud computing realizes the long-held dream of converting computing capability into a type of utility. It has the potential to fundamentally change the landscape of the IT industry and our way of life. However, as cloud computing expanding substantially in both scale and scope, ensuring its sustainable growth is a critical problem. Service providers have long been suffering from high operational costs. Especially the costs associated with the skyrocketing power consumption of large data centers. In the meantime, while efficient power/energy utilization is indispensable for the sustainable growth of cloud computing, service providers must also satisfy a user's quality of service (QoS) requirements. This problem becomes even more challenging considering the increasingly stringent power/energy and QoS constraints, as well as other factors such as the highly dynamic, heterogeneous, and distributed nature of the computing infrastructures, etc. ^ In this dissertation, we study the problem of delay-sensitive cloud service scheduling for the sustainable development of cloud computing. We first focus our research on the development of scheduling methods for delay-sensitive cloud services on a single server with the goal of maximizing a service provider's profit. We then extend our study to scheduling cloud services in distributed environments. In particular, we develop a queue-based model and derive efficient request dispatching and processing decisions in a multi-electricity-market environment to improve the profits for service providers. We next study a problem of multi-tier service scheduling. By carefully assigning sub deadlines to the service tiers, our approach can significantly improve resource usage efficiencies with statistically guaranteed QoS. Finally, we study the power conscious resource provision problem for service requests with different QoS requirements. By properly sharing computing resources among different requests, our method statistically guarantees all QoS requirements with a minimized number of powered-on servers and thus the power consumptions. The significance of our research is that it is one part of the integrated effort from both industry and academia to ensure the sustainable growth of cloud computing as it continues to evolve and change our society profoundly.^

Development of hardware in the loop real-time control techniques for hybrid power systems involving distributed demands and sustainable energy sources

The future power grid will effectively utilize renewable energy resources and distributed generation to respond to energy demand while incorporating information technology and communication infrastructure for their optimum operation. This dissertation contributes to the development of real-time techniques, for wide-area monitoring and secure real-time control and operation of hybrid power systems. ^ To handle the increased level of real-time data exchange, this dissertation develops a supervisory control and data acquisition (SCADA) system that is equipped with a state estimation scheme from the real-time data. This system is verified on a specially developed laboratory-based test bed facility, as a hardware and software platform, to emulate the actual scenarios of a real hybrid power system with the highest level of similarities and capabilities to practical utility systems. It includes phasor measurements at hundreds of measurement points on the system. These measurements were obtained from especially developed laboratory based Phasor Measurement Unit (PMU) that is utilized in addition to existing commercially based PMU’s. The developed PMU was used in conjunction with the interconnected system along with the commercial PMU’s. The tested studies included a new technique for detecting the partially islanded micro grids in addition to several real-time techniques for synchronization and parameter identifications of hybrid systems. ^ Moreover, due to numerous integration of renewable energy resources through DC microgrids, this dissertation performs several practical cases for improvement of interoperability of such systems. Moreover, increased number of small and dispersed generating stations and their need to connect fast and properly into the AC grids, urged this work to explore the challenges that arise in synchronization of generators to the grid and through introduction of a Dynamic Brake system to improve the process of connecting distributed generators to the power grid.^ Real time operation and control requires data communication security. A research effort in this dissertation was developed based on Trusted Sensing Base (TSB) process for data communication security. The innovative TSB approach improves the security aspect of the power grid as a cyber-physical system. It is based on available GPS synchronization technology and provides protection against confidentiality attacks in critical power system infrastructures. ^

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Passivity Framework for Modeling, Composing and Mitigating Cyber Attacks

Thesis (Ph.D.)--University of Washington, 2016-08

Design and Fabrication of Perovskite Micro-Cavity Lasers

Thesis (Master's)--University of Washington, 2016-08

Optics-Based Quantum Information and Sensing Platforms Utilizing the Nitrogen-Vacancy Center in Diamond

Thesis (Ph.D.)--University of Washington, 2016-08