A tremendous amount of information is being shared every day on social media sites such as Facebook, Twitter or Google+. However, only a small portion of users provide their location information, which can be helpful in targeted advertising and many other services. Current methods in location estimation using social relationships consider social friendship as a simple binary relationship. However, social closeness between users and structure of friends have strong implications on geographic distances. In the first task, we introduce new measures to evaluate the social closeness between users and structure of friends. Then we propose models that use them for location estimation. Compared with the models which take the friend relation as a binary feature, social closeness can help identify which friend of a user is more important and friend structure can help to determine significance level of locations, thus improving the accuracy of the location estimation models. A confidence iteration method is further introduced to improve estimation accuracy and overcome the problem of scarce location information. We evaluate our methods on two different datasets, Twitter and Gowalla. The results show that our model can improve the estimation accuracy by 5% - 20% compared with state-of-the-art friend-based models. In the …

The demand for higher data rates for indoor and cell-edge users led to evolution of small cells. LTE femtocells, one of the small cell categories, are low-power low-cost mobile base stations, which are deployed within the coverage area of the traditional macro base station. The cross-tier and co-tier interferences occur only when the macrocell and femtocell share the same frequency channels. Open access (OSG), closed access (CSG), and hybrid access are the three existing access-control methods that decide users' connectivity to the femtocell access point (FAP). We define a network performance function, network productivity, to measure the traffic that is carried successfully. In this dissertation, we evaluate call mobility in LTE integrated network and determine optimized network productivity with variable call arrival rate in given LTE deployment with femtocell access modes (OSG, CSG, HYBRID) for a given call blocking vector. The solution to the optimization is maximum network productivity and call arrival rates for all cells. In the second scenario, we evaluate call mobility in LTE integrated network with increasing femtocells and maximize network productivity with variable femtocells distribution per macrocell with constant call arrival rate in uniform LTE deployment with femtocell access modes (OSG, CSG, HYBRID) for a given …

In this research, the application of intelligent mobile agents to the management of distributed network environments is investigated. Intelligent mobile agents are programs which can move about network systems in a deterministic manner in carrying their execution state. These agents can be considered an application of distributed artificial intelligence where the (usually small) agent code is moved to the data and executed locally. The mobile agent paradigm offers potential advantages over many conventional mechanisms which move (often large) data to the code, thereby wasting available network bandwidth. The performance of agents in network routing and knowledge acquisition has been investigated and simulated. A working mobile agent system has also been designed and implemented in JDK 1.2.

The phenomenal growth of Information Technology requires us to elicit, store and maintain huge volumes of data. Analyzing this data for various purposes is becoming increasingly important. Data mining consists of applying data analysis and discovery algorithms that under acceptable computational efficiency limitations, produce a particular enumeration of patterns over the data. We present two techniques based on using Logic programming tools for data mining. Data mining analyzes data by extracting patterns which describe its structure and discovers co-relations in the form of rules. We distinguish analysis methods as visual and non-visual and present one application of each. We explain that our focus on the field of Logic Programming makes some of the very complex tasks related to Web based data mining and dynamic content generation, simple and easy to implement in a uniform framework.

The fight against epidemics/pandemics is one of man versus nature. Technological advances have not only improved existing methods for monitoring and controlling disease outbreaks, but have also provided new means for investigation, such as through modeling and simulation. This dissertation explores the relationship between social structure and disease dynamics. Social structures are modeled as graphs, and outbreaks are simulated based on a well-recognized standard, the susceptible-infectious-removed (SIR) paradigm. Two independent, but related, studies are presented. The first involves measuring the severity of outbreaks as social network parameters are altered. The second study investigates the efficacy of various vaccination policies based on social structure. Three disease-related centrality measures are introduced, contact, transmission, and spread centrality, which are related to previously established centrality measures degree, betweenness, and closeness, respectively. The results of experiments presented in this dissertation indicate that reducing the neighborhood size along with outside-of-neighborhood contacts diminishes the severity of disease outbreaks. Vaccination strategies can effectively reduce these parameters. Additionally, vaccination policies that target individuals with high centrality are generally shown to be slightly more effective than a random vaccination policy. These results combined with past and future studies will assist public health officials in their effort to minimize the effects …

The presence of naturally occurring and man-made public health threats necessitate the design and implementation of mitigation strategies, such that adequate response is provided in a timely manner. Since multiple variables, such as geographic properties, resource constraints, and government mandated time-frames must be accounted for, computational methods provide the necessary tools to develop contingency response plans while respecting underlying data and assumptions. A typical response scenario involves the placement of points of dispensing (PODs) in the affected geographic region to supply vaccines or medications to the general public. Computational tools aid in the analysis of such response plans, as well as in the strategic placement of PODs, such that feasible response scenarios can be developed. Due to the sensitivity of bio-emergency response plans, geographic information, such as POD locations, must be kept confidential. The generation of synthetic geographic regions allows for the development of emergency response plans on non-sensitive data, as well as for the study of the effects of single geographic parameters. Further, synthetic representations of geographic regions allow for results to be published and evaluated by the scientific community. This dissertation presents methodology for the analysis of bio-emergency response plans, methods for plan optimization, as well as methodology …

Smart phones today have become increasingly popular with the general public for its diverse abilities like navigation, social networking, and multimedia facilities to name a few. These phones are equipped with high end processors, high resolution cameras, built-in sensors like accelerometer, orientation-sensor, light-sensor, and much more. According to comScore survey, 25.3% of US adults use smart phones in their daily lives. Motivated by the capability of smart phones and their extensive usage, I focused on utilizing them for bio-medical applications. In this thesis, I present a new application for a smart phone to quantify the vital signs such as heart rate, respiratory rate and blood pressure with the help of its built-in sensors. Using the camera and a microphone, I have shown how the blood pressure and heart rate can be determined for a subject. People sometimes encounter minor situations like fainting or fatal accidents like car crash at unexpected times and places. It would be useful to have a device which can measure all vital signs in such an event. The second part of this thesis demonstrates a new mode of communication for next generation 9-1-1 calls. In this new architecture, the call-taker will be able to control the …

This dissertation investigates data reduction strategies from a signal processing perspective in centralized detection and estimation applications. First, it considers a deterministic source observed by a network of sensors and develops an analytical strategy for ranking sensor transmissions based on the magnitude of their test statistics. The benefit of the proposed strategy is that the decision to transmit or not to transmit observations to the fusion center can be made at the sensor level resulting in significant savings in transmission costs. A sensor network based on target tracking application is simulated to demonstrate the benefits of the proposed strategy over the unconstrained energy approach. Second, it considers the detection of random signals in noisy measurements and evaluates the performance of eigenvalue-based signal detectors. Due to their computational simplicity, robustness and performance, these detectors have recently received a lot of attention. When the observed random signal is correlated, several researchers claim that the performance of eigenvalue-based detectors exceeds that of the classical energy detector. However, such claims fail to consider the fact that when the signal is correlated, the optimal detector is the estimator-correlator and not the energy detector. In this dissertation, through theoretical analyses and Monte Carlo simulations, eigenvalue-based detectors …

Online/offline signature schemes are useful in many situations, and two such scenarios are considered in this dissertation: bursty server authentication and embedded device authentication. In this dissertation, new techniques for online/offline signing are introduced, those are applied in a variety of ways for creating online/offline signature schemes, and five different online/offline signature schemes that are proved secure under a variety of models and assumptions are proposed. Two of the proposed five schemes have the best offline or best online performance of any currently known technique, and are particularly well-suited for the scenarios that are considered in this dissertation. To determine if the proposed schemes provide the expected practical improvements, a series of experiments were conducted comparing the proposed schemes with each other and with other state-of-the-art schemes in this area, both on a desktop class computer, and under AVR Studio, a simulation platform for an 8-bit processor that is popular for embedded systems. Under AVR Studio, the proposed SGE scheme using a typical key size for the embedded device authentication scenario, can complete the offline phase in about 24 seconds and then produce a signature (the online phase) in 15 milliseconds, which is the best offline performance of any known …

Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent …

The focus of this research is on the development of a real time application that uses a low cost EEG headset to measure a player's state of mind while they play a video game. Using data collected using the Emotiv EPOC headset, various EEG processing techniques are tested to find ways of measuring a person's engagement and arousal levels. The ability to measure a person's engagement and arousal levels provide an opportunity to develop a model that monitor a person's flow while playing video games. Identifying when certain events occur, like when the player dies, will make it easier to identify when a player has left a state of flow. The real time application Brainwave captures data from the wireless Emotiv EPOC headset. Brainwave converts the raw EEG data into more meaningful brainwave band frequencies. Utilizing the brainwave frequencies the program trains multiple machine learning algorithms with data designed to identify when the player dies. Brainwave runs while the player plays through a video gaming monitoring their engagement and arousal levels for changes that cause the player to leave a state of flow. Brainwave reports to researchers and developers when the player dies along with the identification of the players …

Automatically generating questions from text for educational purposes is an active research area in natural language processing. The automatic question generation system accompanying this dissertation is MARGE, which is a recursive acronym for: MARGE automatically reads generates and evaluates. MARGE generates questions from both individual sentences and the passage as a whole, and is the first question generation system to successfully generate meaningful questions from textual units larger than a sentence. Prior work in automatic question generation from text treats a sentence as a string of constituents to be rearranged into as many questions as allowed by English grammar rules. Consequently, such systems overgenerate and create mainly trivial questions. Further, none of these systems to date has been able to automatically determine which questions are meaningful and which are trivial. This is because the research focus has been placed on NLG at the expense of NLU. In contrast, the work presented here infuses the questions generation process with natural language understanding. From the input text, MARGE creates a meaning analysis representation for each sentence in a passage via the DeconStructure algorithm presented in this work. Questions are generated from sentence meaning analysis representations using templates. The generated questions are automatically …

The objective of this thesis is to design a model for a multi global navigation satellite system using Simulink. It explains a design procedure which includes the models for transmitter and receiver for two different navigation systems. To overcome the problem, where less number of satellites are visible to determine location degrades the performance of any positioning system significantly, this research has done to make use of multi GNSS satellite signals in one navigation receiver.

With the advances in very large scale integration (VLSI) technology, hundreds of billions of transistors can be packed into a single chip. With the increased hardware budget, how to take advantage of available hardware resources becomes an important research area. Some researchers have shifted from control flow Von-Neumann architecture back to dataflow architecture again in order to explore scalable architectures leading to multi-core systems with several hundreds of processing elements. In this dissertation, I address how the performance of modern processing systems can be improved, while attempting to reduce hardware complexity and energy consumptions. My research described here tackles both central processing unit (CPU) performance and memory subsystem performance. More specifically I will describe my research related to the design of an innovative decoupled multithreaded architecture that can be used in multi-core processor implementations. I also address how memory management functions can be off-loaded from processing pipelines to further improve system performance and eliminate cache pollution caused by runtime management functions.

Peptide mass fingerprinting (PMF) was the first automated method for protein identification in proteomics, and it remains in common usage today because of its simplicity and the low equipment costs for generating fingerprints. However, one of the problems with PMF is its limited specificity and sensitivity in protein identification. Here I present a method that shows potential to significantly enhance the accuracy of peptide mass fingerprinting, using a machine learning approach based on a hidden Markov model (HMM). This method is applied to improve differentiation of real protein matches from those that occur by chance. The system was trained using 300 examples of combined real and false-positive protein identification results, and 10-fold cross-validation applied to assess model discrimination. The model can achieve 93% accuracy in distinguishing correct and real protein identification results versus false-positive matches. The receiver operating characteristic (ROC) curve area for the best model was 0.833.

The electronic voting machine (EVM) plays a very important role in a country where government officials are elected into office. Throughout the world, a specific operating system that tends to the specific requirement of the EVM does not exist. Existing EVM technology depends upon the various operating systems currently available, thus ignoring the basic needs of the system. There is a compromise over the basic requirements in order to develop the systems on the basis on an already available operating system, thus having a lot of scope for error. It is necessary to know the specific details of the particular device for which the operating system is being developed. In this document, I evaluate existing EVMs and identify flaws and shortcomings. I propose a solution for a new operating system that meets the specific requirements of the EVM, calling it Voting Operating System (VOS, pronounced 'voice'). The identification technique can be simplified by using the fingerprint technology that determines the identity of a person based on two fingerprints. I also discuss the various parts of the operating system that have to be implemented that can tend to all the basic requirements of an EVM, including implementation of the memory manager, …

Advances in integrated circuit technology continue to provide more and more transistors on a chip. Computer architects are faced with the challenge of finding the best way to translate these resources into high performance. The challenge in the design of next generation CPU (central processing unit) lies not on trying to use up the silicon area, but on finding smart ways to make use of the wealth of transistors now available. In addition, the next generation architecture should offer high throughout performance, scalability, modularity, and low energy consumption, instead of an architecture that is suitable for only one class of applications or users, or only emphasize faster clock rate. A program exhibits different types of parallelism: instruction level parallelism (ILP), thread level parallelism (TLP), or data level parallelism (DLP). Likewise, architectures can be designed to exploit one or more of these types of parallelism. It is generally not possible to design architectures that can take advantage of all three types of parallelism without using very complex hardware structures and complex compiler optimizations. We present the state-of-art architecture SDF (scheduled data flowed) which explores the TLP parallelism as much as that is supplied by that application. We implement a SDF single-chip …

Agents that act as user assistants will become invaluable as the number of information sources continue to proliferate. Such agents can support the work of users by learning to automate time-consuming tasks and filter information to manageable levels. Although considerable advances have been made in this area, it remains a fertile area for further development. One application of agents under careful scrutiny is the automated negotiation of conflicts between different user's needs and desires. Many techniques require explicit user models in order to function. This dissertation explores a technique for dynamically constructing user models and the impact of using them to anticipate the need for negotiation. Negotiation is reduced by including an advising aspect to the agent that can use this anticipation of conflict to adjust user behavior.

Designing 802.11 wireless networks includes two major components: selection of access points (APs) in the demand areas and assignment of radio frequencies to each AP. Coverage and capacity are some key issues when placing APs in a demand area. APs need to cover all users. A user is considered covered if the power received from its corresponding AP is greater than a given threshold. Moreover, from a capacity standpoint, APs need to provide certain minimum bandwidth to users located in the coverage area. A major challenge in designing wireless networks is the frequency assignment problem. The 802.11 wireless LANs operate in the unlicensed ISM frequency, and all APs share the same frequency. As a result, as 802.11 APs become widely deployed, they start to interfere with each other and degrade network throughput. In consequence, efficient assignment of channels becomes necessary to avoid and minimize interference. In this work, an optimal AP selection was developed by balancing traffic load. An optimization problem was formulated that minimizes heavy congestion. As a result, APs in wireless LANs will have well distributed traffic loads, which maximize the throughput of the network. The channel assignment algorithm was designed by minimizing channel interference between APs. The …

The Internet and networks in general have become essential tools for disseminating in-formation. Search engines have become the predominant means of finding information on the Web and all other data repositories, including local resources. Domain scientists regularly acquire and analyze images generated by equipment such as microscopes and cameras, resulting in complex image files that need to be managed in a convenient manner. This type of integrated environment has been recently termed a netcentric sci-entific research repository. I developed a number of data manipulation tools that allow researchers to manage their information more effectively in a netcentric environment. The specific contributions are: (1) A unique interface for management of data including files and relational databases. A wrapper for relational databases was developed so that the data can be indexed and searched using traditional search engines. This approach allows data in databases to be searched with the same interface as other data. Fur-thermore, this approach makes it easier for scientists to work with their data if they are not familiar with SQL. (2) A Web services based architecture for integrating analysis op-erations into a repository. This technique allows the system to leverage the large num-ber of existing tools by wrapping them …

Wireless sensor networks are battery-powered ad-hoc networks in which sensor nodes that are scattered over a region connect to each other and form multi-hop networks. These nodes are equipped with sensors such as temperature sensors, pressure sensors, and light sensors and can be queried to get the corresponding values for analysis. However, since they are battery operated, care has to be taken so that these nodes use energy efficiently. One of the areas in sensor networks where an energy analysis can be done is routing. This work explores grid-based coordinated routing in wireless sensor networks and compares the energy available in the network over time for different grid sizes.

I present a time synchronization algorithm for wireless sensor networks that aims to conserve sensor battery power. The proposed method creates a hierarchical tree by flooding the sensor network from a designated source point. It then uses a hybrid algorithm derived from the timing-sync protocol for sensor networks (TSPN) and the reference broadcast synchronization method (RBS) to periodically synchronize sensor clocks by minimizing energy consumption. In multi-hop ad-hoc networks, a depleted sensor will drop information from all other sensors that route data through it, decreasing the physical area being monitored by the network. The proposed method uses several techniques and thresholds to maintain network connectivity. A new root sensor is chosen when the current one's battery power decreases to a designated value. I implement this new synchronization technique using Matlab and show that it can provide significant power savings over both TPSN and RBS.

Modern high performance computing is dependent on parallel processing systems. Most current benchmarks reveal only the high level computational throughput metrics, which may be sufficient for single processor systems, but can lead to a misrepresentation of true system capability for parallel systems. A new benchmark is therefore proposed. CLUE (Cluster Evaluator) uses a cellular automata algorithm to evaluate the scalability of parallel processing machines. The benchmark also uses algorithmic variations to evaluate individual system components' impact on the overall serial fraction and efficiency. CLUE is not a replacement for other performance-centric benchmarks, but rather shows the scalability of a system and provides metrics to reveal where one can improve overall performance. CLUE is a new benchmark which demonstrates a better comparison among different parallel systems than existing benchmarks and can diagnose where a particular parallel system can be optimized.

Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles is also guided by the ontology. The research conducted shows that a system can be created that utilizes statistical text classification methods to extract information from an ad-hoc generated information source like Wikipedia for use in a formal semantic ontology like Cyc. Benefits and limitations of the system are discussed along with future work.