Extended principal component analysis and its applications to image analysis

The objectives of this research are to analyze and develop a modified Principal Component Analysis (PCA) and to develop a two-dimensional PCA with applications in image processing. PCA is a classical multivariate technique where its mathematical treatment is purely based on the eigensystem of positive-definite symmetric matrices. Its main function is to statistically transform a set of correlated variables to a new set of uncorrelated variables over $\IR\sp{n}$ by retaining most of the variations present in the original variables.^ The variances of the Principal Components (PCs) obtained from the modified PCA form a correlation matrix of the original variables. The decomposition of this correlation matrix into a diagonal matrix produces a set of orthonormal basis that can be used to linearly transform the given PCs. It is this linear transformation that reproduces the original variables. The two-dimensional PCA can be devised as a two successive of one-dimensional PCA. It can be shown that, for an $m\times n$ matrix, the PCs obtained from the two-dimensional PCA are the singular values of that matrix.^ In this research, several applications for image analysis based on PCA are developed, i.e., edge detection, feature extraction, and multi-resolution PCA decomposition and reconstruction. ^

Ionized cluster beam deposition: Modeling, cluster size measurement and applications

Clusters are aggregations of atoms or molecules, generally intermediate in size between individual atoms and aggregates that are large enough to be called bulk matter. Clusters can also be called nanoparticles, because their size is on the order of nanometers or tens of nanometers. A new field has begun to take shape called nanostructured materials which takes advantage of these atom clusters. The ultra-small size of building blocks leads to dramatically different properties and it is anticipated that such atomically engineered materials will be able to be tailored to perform as no previous material could.^ The idea of ionized cluster beam (ICB) thin film deposition technique was first proposed by Takagi in 1972. It was based upon using a supersonic jet source to produce, ionize and accelerate beams of atomic clusters onto substrates in a vacuum environment. Conditions for formation of cluster beams suitable for thin film deposition have only recently been established following twenty years of effort. Zinc clusters over 1,000 atoms in average size have been synthesized both in our lab and that of Gspann. More recently, other methods of synthesizing clusters and nanoparticles, using different types of cluster sources, have come under development.^ In this work, we studied different aspects of nanoparticle beams. The work includes refinement of a model of the cluster formation mechanism, development of a new real-time, in situ cluster size measurement method, and study of the use of ICB in the fabrication of semiconductor devices.^ The formation process of the vaporized-metal cluster beam was simulated and investigated using classical nucleation theory and one dimensional gas flow equations. Zinc cluster sizes predicted at the nozzle exit are in good quantitative agreement with experimental results in our laboratory.^ A novel in situ real-time mass, energy and velocity measurement apparatus has been designed, built and tested. This small size time-of-flight mass spectrometer is suitable to be used in our cluster deposition systems and does not suffer from problems related to other methods of cluster size measurement like: requirement for specialized ionizing lasers, inductive electrical or electromagnetic coupling, dependency on the assumption of homogeneous nucleation, limits on the size measurement and non real-time capability. Measured ion energies using the electrostatic energy analyzer are in good accordance with values obtained from computer simulation. The velocity (v) is measured by pulsing the cluster beam and measuring the time of delay between the pulse and analyzer output current. The mass of a particle is calculated from m = (2E/v$\sp2).$ The error in the measured value of background gas mass is on the order of 28% of the mass of one N$\sb2$ molecule which is negligible for the measurement of large size clusters. This resolution in cluster size measurement is very acceptable for our purposes.^ Selective area deposition onto conducting patterns overlying insulating substrates was demonstrated using intense, fully-ionized cluster beams. Parameters influencing the selectivity are ion energy, repelling voltage, the ratio of the conductor to insulator dimension, and substrate thickness. ^

Multipurpose portable system for environmental, industrial, and medical applications

This dissertation is about the research carried on developing an MPS (Multipurpose Portable System) which consists of an instrument and many accessories. The instrument is portable, hand-held, and rechargeable battery operated, and it measures temperature, absorbance, and concentration of samples by using optical principles. The system also performs auxiliary functions like incubation and mixing. This system can be used in environmental, industrial, and medical applications. ^ Research emphasis is on system modularity, easy configuration, accuracy of measurements, power management schemes, reliability, low cost, computer interface, and networking. The instrument can send the data to a computer for data analysis and presentation, or to a printer. ^ This dissertation includes the presentation of a full working system. This involved integration of hardware and firmware for the micro-controller in assembly language, software in C and other application modules. ^ The instrument contains the Optics, Transimpedance Amplifiers, Voltage-to-Frequency Converters, LCD display, Lamp Driver, Battery Charger, Battery Manager, Timer, Interface Port, and Micro-controller. ^ The accessories are a Printer, Data Acquisition Adapter (to transfer the measurements to a computer via the Printer Port and expand the Analog/Digital conversion capability), Car Plug Adapter, and AC Transformer. This system has been fully evaluated for fault tolerance and the schemes will also be presented. ^

Engineering access control for distributed enterprise applications

Access control (AC) is a necessary defense against a large variety of security attacks on the resources of distributed enterprise applications. However, to be effective, AC in some application domains has to be fine-grain, support the use of application-specific factors in authorization decisions, as well as consistently and reliably enforce organization-wide authorization policies across enterprise applications. Because the existing middleware technologies do not provide a complete solution, application developers resort to embedding AC functionality in application systems. This coupling of AC functionality with application logic causes significant problems including tremendously difficult, costly and error prone development, integration, and overall ownership of application software. The way AC for application systems is engineered needs to be changed. ^ In this dissertation, we propose an architectural approach for engineering AC mechanisms to address the above problems. First, we develop a framework for implementing the role-based access control (RBAC) model using AC mechanisms provided by CORBA Security. For those application domains where the granularity of CORBA controls and the expressiveness of RBAC model suffice, our framework addresses the stated problem. ^ In the second and main part of our approach, we propose an architecture for an authorization service, RAD, to address the problem of controlling access to distributed application resources, when the granularity and support for complex policies by middleware AC mechanisms are inadequate. Applying this architecture, we developed a CORBA-based application authorization service (CAAS). Using CAAS, we studied the main properties of the architecture and showed how they can be substantiated by employing CORBA and Java technologies. Our approach enables a wide-ranging solution for controlling the resources of distributed enterprise applications. ^

Semantic distributed database management with applications to the Internet dissemination of environmental data

The research presented in this dissertation is comprised of several parts which jointly attain the goal of Semantic Distributed Database Management with Applications to Internet Dissemination of Environmental Data. ^ Part of the research into more effective and efficient data management has been pursued through enhancements to the Semantic Binary Object-Oriented database (Sem-ODB) such as more effective load balancing techniques for the database engine, and the use of Sem-ODB as a tool for integrating structured and unstructured heterogeneous data sources. Another part of the research in data management has pursued methods for optimizing queries in distributed databases through the intelligent use of network bandwidth; this has applications in networks that provide varying levels of Quality of Service or throughput. ^ The application of the Semantic Binary database model as a tool for relational database modeling has also been pursued. This has resulted in database applications that are used by researchers at the Everglades National Park to store environmental data and to remotely-sensed imagery. ^ The areas of research described above have contributed to the creation TerraFly, which provides for the dissemination of geospatial data via the Internet. TerraFly research presented herein ranges from the development of TerraFly's back-end database and interfaces, through the features that are presented to the public (such as the ability to provide autopilot scripts and on-demand data about a point), to applications of TerraFly in the areas of hazard mitigation, recreation, and aviation. ^

Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

Algebraic theory of minimal nondeterministic finite automata with applications

Since the 1950s, the theory of deterministic and nondeterministic finite automata (DFAs and NFAs, respectively) has been a cornerstone of theoretical computer science. In this dissertation, our main object of study is minimal NFAs. In contrast with minimal DFAs, minimal NFAs are computationally challenging: first, there can be more than one minimal NFA recognizing a given language; second, the problem of converting an NFA to a minimal equivalent NFA is NP-hard, even for NFAs over a unary alphabet. Our study is based on the development of two main theories, inductive bases and partials, which in combination form the foundation for an incremental algorithm, ibas, to find minimal NFAs. An inductive basis is a collection of languages with the property that it can generate (through union) each of the left quotients of its elements. We prove a fundamental characterization theorem which says that a language can be recognized by an n-state NFA if and only if it can be generated by an n-element inductive basis. A partial is an incompletely-specified language. We say that an NFA recognizes a partial if its language extends the partial, meaning that the NFA’s behavior is unconstrained on unspecified strings; it follows that a minimal NFA for a partial is also minimal for its language. We therefore direct our attention to minimal NFAs recognizing a given partial. Combining inductive bases and partials, we generalize our characterization theorem, showing that a partial can be recognized by an n-state NFA if and only if it can be generated by an n-element partial inductive basis. We apply our theory to develop and implement ibas, an incremental algorithm that finds minimal partial inductive bases generating a given partial. In the case of unary languages, ibas can often find minimal NFAs of up to 10 states in about an hour of computing time; with brute-force search this would require many trillions of years.

A research platform for artificial neural networks with applications in pediatric epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that (1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and (2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Towards simulation and emulation of large-scale computer networks

Developing analytical models that can accurately describe behaviors of Internet-scale networks is difficult. This is due, in part, to the heterogeneous structure, immense size and rapidly changing properties of today's networks. The lack of analytical models makes large-scale network simulation an indispensable tool for studying immense networks. However, large-scale network simulation has not been commonly used to study networks of Internet-scale. This can be attributed to three factors: 1) current large-scale network simulators are geared towards simulation research and not network research, 2) the memory required to execute an Internet-scale model is exorbitant, and 3) large-scale network models are difficult to validate. This dissertation tackles each of these problems. ^ First, this work presents a method for automatically enabling real-time interaction, monitoring, and control of large-scale network models. Network researchers need tools that allow them to focus on creating realistic models and conducting experiments. However, this should not increase the complexity of developing a large-scale network simulator. This work presents a systematic approach to separating the concerns of running large-scale network models on parallel computers and the user facing concerns of configuring and interacting with large-scale network models. ^ Second, this work deals with reducing memory consumption of network models. As network models become larger, so does the amount of memory needed to simulate them. This work presents a comprehensive approach to exploiting structural duplications in network models to dramatically reduce the memory required to execute large-scale network experiments. ^ Lastly, this work addresses the issue of validating large-scale simulations by integrating real protocols and applications into the simulation. With an emulation extension, a network simulator operating in real-time can run together with real-world distributed applications and services. As such, real-time network simulation not only alleviates the burden of developing separate models for applications in simulation, but as real systems are included in the network model, it also increases the confidence level of network simulation. This work presents a scalable and flexible framework to integrate real-world applications with real-time simulation.^

Practical dynamic thermal management on Intel desktop computer

Fueled by increasing human appetite for high computing performance, semiconductor technology has now marched into the deep sub-micron era. As transistor size keeps shrinking, more and more transistors are integrated into a single chip. This has increased tremendously the power consumption and heat generation of IC chips. The rapidly growing heat dissipation greatly increases the packaging/cooling costs, and adversely affects the performance and reliability of a computing system. In addition, it also reduces the processor's life span and may even crash the entire computing system. Therefore, dynamic thermal management (DTM) is becoming a critical problem in modern computer system design. Extensive theoretical research has been conducted to study the DTM problem. However, most of them are based on theoretically idealized assumptions or simplified models. While these models and assumptions help to greatly simplify a complex problem and make it theoretically manageable, practical computer systems and applications must deal with many practical factors and details beyond these models or assumptions. The goal of our research was to develop a test platform that can be used to validate theoretical results on DTM under well-controlled conditions, to identify the limitations of existing theoretical results, and also to develop new and practical DTM techniques. This dissertation details the background and our research efforts in this endeavor. Specifically, in our research, we first developed a customized test platform based on an Intel desktop. We then tested a number of related theoretical works and examined their limitations under the practical hardware environment. With these limitations in mind, we developed a new reactive thermal management algorithm for single-core computing systems to optimize the throughput under a peak temperature constraint. We further extended our research to a multicore platform and developed an effective proactive DTM technique for throughput maximization on multicore processor based on task migration and dynamic voltage frequency scaling technique. The significance of our research lies in the fact that our research complements the current extensive theoretical research in dealing with increasingly critical thermal problems and enabling the continuous evolution of high performance computing systems.

Leveraging symbiotic relationships for emulation of computer networks

The lack of analytical models that can accurately describe large-scale networked systems makes empirical experimentation indispensable for understanding complex behaviors. Research on network testbeds for testing network protocols and distributed services, including physical, emulated, and federated testbeds, has made steady progress. Although the success of these testbeds is undeniable, they fail to provide: 1) scalability, for handling large-scale networks with hundreds or thousands of hosts and routers organized in different scenarios, 2) flexibility, for testing new protocols or applications in diverse settings, and 3) inter-operability, for combining simulated and real network entities in experiments. This dissertation tackles these issues in three different dimensions. First, we present SVEET, a system that enables inter-operability between real and simulated hosts. In order to increase the scalability of networks under study, SVEET enables time-dilated synchronization between real hosts and the discrete-event simulator. Realistic TCP congestion control algorithms are implemented in the simulator to allow seamless interactions between real and simulated hosts. SVEET is validated via extensive experiments and its capabilities are assessed through case studies involving real applications. Second, we present PrimoGENI, a system that allows a distributed discrete-event simulator, running in real-time, to interact with real network entities in a federated environment. PrimoGENI greatly enhances the flexibility of network experiments, through which a great variety of network conditions can be reproduced to examine what-if questions. Furthermore, PrimoGENI performs resource management functions, on behalf of the user, for instantiating network experiments on shared infrastructures. Finally, to further increase the scalability of network testbeds to handle large-scale high-capacity networks, we present a novel symbiotic simulation approach. We present SymbioSim, a testbed for large-scale network experimentation where a high-performance simulation system closely cooperates with an emulation system in a mutually beneficial way. On the one hand, the simulation system benefits from incorporating the traffic metadata from real applications in the emulation system to reproduce the realistic traffic conditions. On the other hand, the emulation system benefits from receiving the continuous updates from the simulation system to calibrate the traffic between real applications. Specific techniques that support the symbiotic approach include: 1) a model downscaling scheme that can significantly reduce the complexity of the large-scale simulation model, resulting in an efficient emulation system for modulating the high-capacity network traffic between real applications; 2) a queuing network model for the downscaled emulation system to accurately represent the network effects of the simulated traffic; and 3) techniques for reducing the synchronization overhead between the simulation and emulation systems.

Off-line and on-line affective recognition of a computer user through a biosignal processing approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^

A multimodal and adaptive real-time human -computer interface for peoples with severe motor disability

This dissertation introduces the design of a multimodal, adaptive real-time assistive system as an alternate human computer interface that can be used by individuals with severe motor disabilities. The proposed design is based on the integration of a remote eye-gaze tracking system, voice recognition software, and a virtual keyboard. The methodology relies on a user profile that customizes eye gaze tracking using neural networks. The user profiling feature facilitates the notion of universal access to computing resources for a wide range of applications such as web browsing, email, word processing and editing. ^ The study is significant in terms of the integration of key algorithms to yield an adaptable and multimodal interface. The contributions of this dissertation stem from the following accomplishments: (a) establishment of the data transport mechanism between the eye-gaze system and the host computer yielding to a significantly low failure rate of 0.9%; (b) accurate translation of eye data into cursor movement through congregate steps which conclude with calibrated cursor coordinates using an improved conversion function; resulting in an average reduction of 70% of the disparity between the point of gaze and the actual position of the mouse cursor, compared with initial findings; (c) use of both a moving average and a trained neural network in order to minimize the jitter of the mouse cursor, which yield an average jittering reduction of 35%; (d) introduction of a new mathematical methodology to measure the degree of jittering of the mouse trajectory; (e) embedding an onscreen keyboard to facilitate text entry, and a graphical interface that is used to generate user profiles for system adaptability. ^ The adaptability nature of the interface is achieved through the establishment of user profiles, which may contain the jittering and voice characteristics of a particular user as well as a customized list of the most commonly used words ordered according to the user's preferences: in alphabetical or statistical order. This allows the system to successfully provide the capability of interacting with a computer. Every time any of the sub-system is retrained, the accuracy of the interface response improves even more. ^

Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Real-time scheduling of embedded applications on multi-core platforms

For the past several decades, we have experienced the tremendous growth, in both scale and scope, of real-time embedded systems, thanks largely to the advances in IC technology. However, the traditional approach to get performance boost by increasing CPU frequency has been a way of past. Researchers from both industry and academia are turning their focus to multi-core architectures for continuous improvement of computing performance. In our research, we seek to develop efficient scheduling algorithms and analysis methods in the design of real-time embedded systems on multi-core platforms. Real-time systems are the ones with the response time as critical as the logical correctness of computational results. In addition, a variety of stringent constraints such as power/energy consumption, peak temperature and reliability are also imposed to these systems. Therefore, real-time scheduling plays a critical role in design of such computing systems at the system level. We started our research by addressing timing constraints for real-time applications on multi-core platforms, and developed both partitioned and semi-partitioned scheduling algorithms to schedule fixed priority, periodic, and hard real-time tasks on multi-core platforms. Then we extended our research by taking temperature constraints into consideration. We developed a closed-form solution to capture temperature dynamics for a given periodic voltage schedule on multi-core platforms, and also developed three methods to check the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research by incorporating the power/energy constraint with thermal awareness into our research problem. We investigated the energy estimation problem on multi-core platforms, and developed a computation efficient method to calculate the energy consumption for a given voltage schedule on a multi-core platform. In this dissertation, we present our research in details and demonstrate the effectiveness and efficiency of our approaches with extensive experimental results.