UNT Libraries - 57 Matching Results

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Capacity and Throughput Optimization in Multi-cell 3G WCDMA Networks

Description: User modeling enables in the computation of the traffic density in a cellular network, which can be used to optimize the placement of base stations and radio network controllers as well as to analyze the performance of resource management algorithms towards meeting the final goal: the calculation and maximization of network capacity and throughput for different data rate services. An analytical model is presented for approximating the user distributions in multi-cell third generation wideband code division multiple access (WCDMA) networks using 2-dimensional Gaussian distributions by determining the means and the standard deviations of the distributions for every cell. This model allows for the calculation of the inter-cell interference and the reverse-link capacity of the network. An analytical model for optimizing capacity in multi-cell WCDMA networks is presented. Capacity is optimized for different spreading factors and for perfect and imperfect power control. Numerical results show that the SIR threshold for the received signals is decreased by 0.5 to 1.5 dB due to the imperfect power control. The results also show that the determined parameters of the 2-dimensional Gaussian model match well with traditional methods for modeling user distribution. A call admission control algorithm is designed that maximizes the throughput in multi-cell WCDMA networks. Numerical results are presented for different spreading factors and for several mobility scenarios. Our methods of optimizing capacity and throughput are computationally efficient, accurate, and can be implemented in large WCDMA networks.
Date: December 2005
Creator: Nguyen, Son

Resource Management in Wireless Networks

Description: A local call admission control (CAC) algorithm for third generation wireless networks was designed and implemented, which allows for the simulation of network throughput for different spreading factors and various mobility scenarios. A global CAC algorithm is also implemented and used as a benchmark since it is inherently optimized; it yields the best possible performance but has an intensive computational complexity. Optimized local CAC algorithm achieves similar performance as global CAC algorithm at a fraction of the computational cost. Design of a dynamic channel assignment algorithm for IEEE 802.11 wireless systems is also presented. Channels are assigned dynamically depending on the minimal interference generated by the neighboring access points on a reference access point. Analysis of dynamic channel assignment algorithm shows an improvement by a factor of 4 over the default settings of having all access points use the same channel, resulting significantly higher network throughput.
Date: August 2006
Creator: Arepally, Anurag

Comparative Study of RSS-Based Collaborative Localization Methods in Wireless Sensor Networks

Description: In this thesis two collaborative localization techniques are studied: multidimensional scaling (MDS) and maximum likelihood estimator (MLE). A synthesis of a new location estimation method through a serial integration of these two techniques, such that an estimate is first obtained using MDS and then MLE is employed to fine-tune the MDS solution, was the subject of this research using various simulation and experimental studies. In the simulations, important issues including the effects of sensor node density, reference node density and different deployment strategies of reference nodes were addressed. In the experimental study, the path loss model of indoor environments is developed by determining the environment-specific parameters from the experimental measurement data. Then, the empirical path loss model is employed in the analysis and simulation study of the performance of collaborative localization techniques.
Date: December 2006
Creator: Koneru, Avanthi

Models to Combat Email Spam Botnets and Unwanted Phone Calls

Description: With the amount of email spam received these days it is hard to imagine that spammers act individually. Nowadays, most of the spam emails have been sent from a collection of compromised machines controlled by some spammers. These compromised computers are often called bots, using which the spammers can send massive volume of spam within a short period of time. The motivation of this work is to understand and analyze the behavior of spammers through a large collection of spam mails. My research examined a the data set collected over a 2.5-year period and developed an algorithm which would give the botnet features and then classify them into various groups. Principal component analysis was used to study the association patterns of group of spammers and the individual behavior of a spammer in a given domain. This is based on the features which capture maximum variance of information we have clustered. Presence information is a growing tool towards more efficient communication and providing new services and features within a business setting and much more. The main contribution in my thesis is to propose the willingness estimator that can estimate the callee's willingness without his/her involvement, the model estimates willingness level based on call history. Finally, the accuracy of the proposed willingness estimator is validated with the actual call logs.
Date: May 2008
Creator: Husna, Husain

Non-Uniform Grid-Based Coordinated Routing in Wireless Sensor Networks

Description: Wireless sensor networks are ad hoc networks of tiny battery powered sensor nodes that can organize themselves to form self-organized networks and collect information regarding temperature, light, and pressure in an area. Though the applications of sensor networks are very promising, sensor nodes are limited in their capability due to many factors. The main limitation of these battery powered nodes is energy. Sensor networks are expected to work for long periods of time once deployed and it becomes important to conserve the battery life of the nodes to extend network lifetime. This work examines non-uniform grid-based routing protocol as an effort to minimize energy consumption in the network and extend network lifetime. The entire test area is divided into non-uniformly shaped grids. Fixed source and sink nodes with unlimited energy are placed in the network. Sensor nodes with full battery life are deployed uniformly and randomly in the field. The source node floods the network with only the coordinator node active in each grid and the other nodes sleeping. The sink node traces the same route back to the source node through the same coordinators. This process continues till a coordinator node runs out of energy, when new coordinator nodes are elected to participate in routing. Thus the network stays alive till the link between the source and sink nodes is lost, i.e., the network is partitioned. This work explores the efficiency of the non-uniform grid-based routing protocol for different node densities and the non-uniform grid structure that best extends network lifetime.
Date: August 2008
Creator: Kadiyala, Priyanka

Variability-aware low-power techniques for nanoscale mixed-signal circuits.

Description: New circuit design techniques that accommodate lower supply voltages necessary for portable systems need to be integrated into the semiconductor intellectual property (IP) core. Systems that once worked at 3.3 V or 2.5 V now need to work at 1.8 V or lower, without causing any performance degradation. Also, the fluctuation of device characteristics caused by process variation in nanometer technologies is seen as design yield loss. The numerous parasitic effects induced by layouts, especially for high-performance and high-speed circuits, pose a problem for IC design. Lack of exact layout information during circuit sizing leads to long design iterations involving time-consuming runs of complex tools. There is a strong need for low-power, high-performance, parasitic-aware and process-variation-tolerant circuit design. This dissertation proposes methodologies and techniques to achieve variability, power, performance, and parasitic-aware circuit designs. Three approaches are proposed: the single iteration automatic approach, the hybrid Monte Carlo and design of experiments (DOE) approach, and the corner-based approach. Widely used mixed-signal circuits such as analog-to-digital converter (ADC), voltage controlled oscillator (VCO), voltage level converter and active pixel sensor (APS) have been designed at nanoscale complementary metal oxide semiconductor (CMOS) and subjected to the proposed methodologies. The effectiveness of the proposed methodologies has been demonstrated through exhaustive simulations. Apart from these methodologies, the application of dual-oxide and dual-threshold techniques at circuit level in order to minimize power and leakage is also explored.
Date: May 2009
Creator: Ghai, Dhruva V.

Social Network Simulation and Mining Social Media to Advance Epidemiology

Description: Traditional Public Health decision-support can benefit from the Web and social media revolution. This dissertation presents approaches to mining social media benefiting public health epidemiology. Through discovery and analysis of trends in Influenza related blogs, a correlation to Centers for Disease Control and Prevention (CDC) influenza-like-illness patient reporting at sentinel health-care providers is verified. A second approach considers personal beliefs of vaccination in social media. A vaccine for human papillomavirus (HPV) was approved by the Food and Drug Administration in May 2006. The virus is present in nearly all cervical cancers and implicated in many throat and oral cancers. Results from automatic sentiment classification of HPV vaccination beliefs are presented which will enable more accurate prediction of the vaccine's population-level impact. Two epidemic models are introduced that embody the intimate social networks related to HPV transmission. Ultimately, aggregating these methodologies with epidemic and social network modeling facilitate effective development of strategies for targeted interventions.
Date: August 2009
Creator: Corley, Courtney David

E‐Shape Analysis

Description: The motivation of this work is to understand E-shape analysis and how it can be applied to various classification tasks. It has a powerful feature to not only look at what information is contained, but rather how that information looks. This new technique gives E-shape analysis the ability to be language independent and to some extent size independent. In this thesis, I present a new mechanism to characterize an email without using content or context called E-shape analysis for email. I explore the applications of the email shape by carrying out a case study; botnet detection and two possible applications: spam filtering and social-context based finger printing. The second part of this thesis takes what I apply E-shape analysis to activity recognition of humans. Using the Android platform and a T-Mobile G1 phone I collect data from the triaxial accelerometer and use it to classify the motion behavior of a subject.
Date: December 2009
Creator: Sroufe, Paul

Inferring Social and Internal Context Using a Mobile Phone

Description: This dissertation is composed of research studies that contribute to three research areas including social context-aware computing, internal context-aware computing, and human behavioral data mining. In social context-aware computing, four studies are conducted. First, mobile phone user calling behavioral patterns are characterized in forms of randomness level where relationships among them are then identified. Next, a study is conducted to investigate the relationship between the calling behavior and organizational groups. Third, a method is presented to quantitatively define mobile social closeness and social groups, which are then used to identify social group sizes and scaling ratio. Last, based on the mobile social grouping framework, the significant role of social ties in communication patterns is revealed. In internal context-aware computing, two studies are conducted where the notions of internal context are intention and situation. For intentional context, the goal is to sense the intention of the user in placing calls. A model is thus presented for predicting future calls envisaged as a call predicted list (CPL), which makes use of call history to build a probabilistic model of calling behavior. As an incoming call predictor, CPL is a list of numbers/contacts that are the most likely to be the callers within the next hour(s), which is useful for scheduling and daily planning. As an outgoing call predictor, CPL is generated as a list of numbers/contacts that are the most likely to be dialed when the user attempts to make an outgoing call (e.g., by flipping open or unlocking the phone). This feature helps save time from having to search through a lengthy phone book. For situational context, a model is presented for sensing the user's situation (e.g., in a library, driving a car, etc.) based on embedded sensors. The sensed context is then used to switch the phone into a suitable ...
Date: December 2009
Creator: Phithakkitnukoon, Santi

The Value of Everything: Ranking and Association with Encyclopedic Knowledge

Description: This dissertation describes WikiRank, an unsupervised method of assigning relative values to elements of a broad coverage encyclopedic information source in order to identify those entries that may be relevant to a given piece of text. The valuation given to an entry is based not on textual similarity but instead on the links that associate entries, and an estimation of the expected frequency of visitation that would be given to each entry based on those associations in context. This estimation of relative frequency of visitation is embodied in modifications to the random walk interpretation of the PageRank algorithm. WikiRank is an effective algorithm to support natural language processing applications. It is shown to exceed the performance of previous machine learning algorithms for the task of automatic topic identification, providing results comparable to that of human annotators. Second, WikiRank is found useful for the task of recognizing text-based paraphrases on a semantic level, by comparing the distribution of attention generated by two pieces of text using the encyclopedic resource as a common reference. Finally, WikiRank is shown to have the ability to use its base of encyclopedic knowledge to recognize terms from different ontologies as describing the same thing, and thus allowing for the automatic generation of mapping links between ontologies. The conclusion of this thesis is that the "knowledge access heuristic" is valuable and that a ranking process based on a large encyclopedic resource can form the basis for an extendable general purpose mechanism capable of identifying relevant concepts by association, which in turn can be effectively utilized for enumeration and comparison at a semantic level.
Date: December 2009
Creator: Coursey, Kino High

The Influence of Social Network Graph Structure on Disease Dynamics in a Simulated Environment

Description: 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 of inevitable disease epidemics/pandemics.
Date: December 2010
Creator: Johnson, Tina V.

Process-Voltage-Temperature Aware Nanoscale Circuit Optimization

Description: Embedded systems which are targeted towards portable applications are required to have low power consumption because such portable devices are typically powered by batteries. During the memory accesses of such battery operated portable systems, including laptops, cell phones and other devices, a significant amount of power or energy is consumed which significantly affects the battery life. Therefore, efficient and leakage power saving cache designs are needed for longer operation of battery powered applications. Design engineers have limited control over many design parameters of the circuit and hence face many chal-lenges due to inherent process technology variations, particularly on static random access memory (SRAM) circuit design. As CMOS process technologies scale down deeper into the nanometer regime, the push for high performance and reliable systems becomes even more challenging. As a result, developing low-power designs while maintaining better performance of the circuit becomes a very difficult task. Furthermore, a major need for accurate analysis and optimization of various forms of total power dissipation and performance in nanoscale CMOS technologies, particularly in SRAMs, is another critical issue to be considered. This dissertation proposes power-leakage and static noise margin (SNM) analysis and methodologies to achieve optimized static random access memories (SRAMs). Alternate topologies of SRAMs, mainly a 7-transistor SRAM, are taken as a case study throughout this dissertation. The optimized cache designs are process-voltage-temperature (PVT) tolerant and consider individual cells as well as memory arrays.
Date: December 2010
Creator: Thakral, Garima

Exploring Privacy in Location-based Services Using Cryptographic Protocols

Description: Location-based services (LBS) are available on a variety of mobile platforms like cell phones, PDA's, etc. and an increasing number of users subscribe to and use these services. Two of the popular models of information flow in LBS are the client-server model and the peer-to-peer model, in both of which, existing approaches do not always provide privacy for all parties concerned. In this work, I study the feasibility of applying cryptographic protocols to design privacy-preserving solutions for LBS from an experimental and theoretical standpoint. In the client-server model, I construct a two-phase framework for processing nearest neighbor queries using combinations of cryptographic protocols such as oblivious transfer and private information retrieval. In the peer-to-peer model, I present privacy preserving solutions for processing group nearest neighbor queries in the semi-honest and dishonest adversarial models. I apply concepts from secure multi-party computation to realize our constructions and also leverage the capabilities of trusted computing technology, specifically TPM chips. My solution for the dishonest adversarial model is also of independent cryptographic interest. I prove my constructions secure under standard cryptographic assumptions and design experiments for testing the feasibility or practicability of our constructions and benchmark key operations. My experiments show that the proposed constructions are practical to implement and have reasonable costs, while providing strong privacy assurances.
Date: May 2011
Creator: Vishwanathan, Roopa

Physical-Layer Network Coding for MIMO Systems

Description: The future wireless communication systems are required to meet the growing demands of reliability, bandwidth capacity, and mobility. However, as corruptions such as fading effects, thermal noise, are present in the channel, the occurrence of errors is unavoidable. Motivated by this, the work in this dissertation attempts to improve the system performance by way of exploiting schemes which statistically reduce the error rate, and in turn boost the system throughput. The network can be studied using a simplified model, the two-way relay channel, where two parties exchange messages via the assistance of a relay in between. In such scenarios, this dissertation performs theoretical analysis of the system, and derives closed-form and upper bound expressions of the error probability. These theoretical measurements are potentially helpful references for the practical system design. Additionally, several novel transmission methods including block relaying, permutation modulations for the physical-layer network coding, are proposed and discussed. Numerical simulation results are presented to support the validity of the conclusions.
Date: May 2011
Creator: Xu, Ning

Joint Schemes for Physical Layer Security and Error Correction

Description: The major challenges facing resource constraint wireless devices are error resilience, security and speed. Three joint schemes are presented in this research which could be broadly divided into error correction based and cipher based. The error correction based ciphers take advantage of the properties of LDPC codes and Nordstrom Robinson code. A cipher-based cryptosystem is also presented in this research. The complexity of this scheme is reduced compared to conventional schemes. The securities of the ciphers are analyzed against known-plaintext and chosen-plaintext attacks and are found to be secure. Randomization test was also conducted on these schemes and the results are presented. For the proof of concept, the schemes were implemented in software and hardware and these shows a reduction in hardware usage compared to conventional schemes. As a result, joint schemes for error correction and security provide security to the physical layer of wireless communication systems, a layer in the protocol stack where currently little or no security is implemented. In this physical layer security approach, the properties of powerful error correcting codes are exploited to deliver reliability to the intended parties, high security against eavesdroppers and efficiency in communication system. The notion of a highly secure and reliable physical layer has the potential to significantly change how communication system designers and users think of the physical layer since the error control codes employed in this work will have the dual roles of both reliability and security.
Date: August 2011
Creator: Adamo, Oluwayomi Bamidele

Scene Analysis Using Scale Invariant Feature Extraction and Probabilistic Modeling

Description: Conventional pattern recognition systems have two components: feature analysis and pattern classification. For any object in an image, features could be considered as the major characteristic of the object either for object recognition or object tracking purpose. Features extracted from a training image, can be used to identify the object when attempting to locate the object in a test image containing many other objects. To perform reliable scene analysis, it is important that the features extracted from the training image are detectable even under changes in image scale, noise and illumination. Scale invariant feature has wide applications such as image classification, object recognition and object tracking in the image processing area. In this thesis, color feature and SIFT (scale invariant feature transform) are considered to be scale invariant feature. The classification, recognition and tracking result were evaluated with novel evaluation criterion and compared with some existing methods. I also studied different types of scale invariant feature for the purpose of solving scene analysis problems. I propose probabilistic models as the foundation of analysis scene scenario of images. In order to differential the content of image, I develop novel algorithms for the adaptive combination for multiple features extracted from images. I demonstrate the performance of the developed algorithm on several scene analysis tasks, including object tracking, video stabilization, medical video segmentation and scene classification.
Date: August 2011
Creator: Shen, Yao

Indoor Localization Using Magnetic Fields

Description: Indoor localization consists of locating oneself inside new buildings. GPS does not work indoors due to multipath reflection and signal blockage. WiFi based systems assume ubiquitous availability and infrastructure based systems require expensive installations, hence making indoor localization an open problem. This dissertation consists of solving the problem of indoor localization by thoroughly exploiting the indoor ambient magnetic fields comprising mainly of disturbances termed as anomalies in the Earth’s magnetic field caused by pillars, doors and elevators in hallways which are ferromagnetic in nature. By observing uniqueness in magnetic signatures collected from different campus buildings, the work presents the identification of landmarks and guideposts from these signatures and further develops magnetic maps of buildings - all of which can be used to locate and navigate people indoors. To understand the reason behind these anomalies, first a comparison between the measured and model generated Earth’s magnetic field is made, verifying the presence of a constant field without any disturbances. Then by modeling the magnetic field behavior of different pillars such as steel reinforced concrete, solid steel, and other structures like doors and elevators, the interaction of the Earth’s field with the ferromagnetic fields is described thereby explaining the causes of the uniqueness in the signatures that comprise these disturbances. Next, by employing the dynamic time warping algorithm to account for time differences in signatures obtained from users walking at different speeds, an indoor localization application capable of classifying locations using the magnetic signatures is developed solely on the smart phone. The application required users to walk short distances of 3-6 m anywhere in hallway to be located with accuracies of 80-99%. The classification framework was further validated with over 90% accuracies using model generated magnetic signatures representing hallways with different kinds of pillars, doors and elevators. All in all, this dissertation ...
Date: December 2011
Creator: Pathapati Subbu, Kalyan Sasidhar

Incremental Learning with Large Datasets

Description: This dissertation focuses on the novel learning strategy based on geometric support vector machines to address the difficulties of processing immense data set. Support vector machines find the hyper-plane that maximizes the margin between two classes, and the decision boundary is represented with a few training samples it becomes a favorable choice for incremental learning. The dissertation presents a novel method Geometric Incremental Support Vector Machines (GISVMs) to address both efficiency and accuracy issues in handling massive data sets. In GISVM, skin of convex hulls is defined and an efficient method is designed to find the best skin approximation given available examples. The set of extreme points are found by recursively searching along the direction defined by a pair of known extreme points. By identifying the skin of the convex hulls, the incremental learning will only employ a much smaller number of samples with comparable or even better accuracy. When additional samples are provided, they will be used together with the skin of the convex hull constructed from previous dataset. This results in a small number of instances used in incremental steps of the training process. Based on the experimental results with synthetic data sets, public benchmark data sets from UCI and endoscopy videos, it is evident that the GISVM achieved satisfactory classifiers that closely model the underlying data distribution. GISVM improves the performance in sensitivity in the incremental steps, significantly reduced the demand for memory space, and demonstrates the ability of recovery from temporary performance degradation.
Date: May 2012
Creator: Giritharan, Balathasan

Metamodeling-based Fast Optimization of Nanoscale Ams-socs

Description: Modern consumer electronic systems are mostly based on analog and digital circuits and are designed as analog/mixed-signal systems on chip (AMS-SoCs). the integration of analog and digital circuits on the same die makes the system cost effective. in AMS-SoCs, analog and mixed-signal portions have not traditionally received much attention due to their complexity. As the fabrication technology advances, the simulation times for AMS-SoC circuits become more complex and take significant amounts of time. the time allocated for the circuit design and optimization creates a need to reduce the simulation time. the time constraints placed on designers are imposed by the ever-shortening time to market and non-recurrent cost of the chip. This dissertation proposes the use of a novel method, called metamodeling, and intelligent optimization algorithms to reduce the design time. Metamodel-based ultra-fast design flows are proposed and investigated. Metamodel creation is a one time process and relies on fast sampling through accurate parasitic-aware simulations. One of the targets of this dissertation is to minimize the sample size while retaining the accuracy of the model. in order to achieve this goal, different statistical sampling techniques are explored and applied to various AMS-SoC circuits. Also, different metamodel functions are explored for their accuracy and application to AMS-SoCs. Several different optimization algorithms are compared for global optimization accuracy and convergence. Three different AMS circuits, ring oscillator, inductor-capacitor voltage-controlled oscillator (LC-VCO) and phase locked loop (PLL) that are present in many AMS-SoC are used in this study for design flow application. Metamodels created in this dissertation provide accuracy with an error of less than 2% from the physical layout simulations. After optimal sampling investigation, metamodel functions and optimization algorithms are ranked in terms of speed and accuracy. Experimental results show that the proposed design flow provides roughly 5,000x speedup over conventional design flows. Thus, ...
Date: May 2012
Creator: Garitselov, Oleg

Sentence Similarity Analysis with Applications in Automatic Short Answer Grading

Description: In this dissertation, I explore unsupervised techniques for the task of automatic short answer grading. I compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. I continue to combine graph alignment features with lexical semantic similarity measures and employ machine learning techniques to show that grade assignment error can be reduced compared to a system that considers only lexical semantic measures of similarity. I also detail a preliminary attempt to align the dependency graphs of student and instructor answers in order to utilize a structural component that is necessary to simulate human-level grading of student answers. I further explore the utility of these techniques to several related tasks in natural language processing including the detection of text similarity, paraphrase, and textual entailment.
Date: August 2012
Creator: Mohler, Michael A.G.

Modeling Synergistic Relationships Between Words and Images

Description: Texts and images provide alternative, yet orthogonal views of the same underlying cognitive concept. By uncovering synergistic, semantic relationships that exist between words and images, I am working to develop novel techniques that can help improve tasks in natural language processing, as well as effective models for text-to-image synthesis, image retrieval, and automatic image annotation. Specifically, in my dissertation, I will explore the interoperability of features between language and vision tasks. In the first part, I will show how it is possible to apply features generated using evidence gathered from text corpora to solve the image annotation problem in computer vision, without the use of any visual information. In the second part, I will address research in the reverse direction, and show how visual cues can be used to improve tasks in natural language processing. Importantly, I propose a novel metric to estimate the similarity of words by comparing the visual similarity of concepts invoked by these words, and show that it can be used further to advance the state-of-the-art methods that employ corpus-based and knowledge-based semantic similarity measures. Finally, I attempt to construct a joint semantic space connecting words with images, and synthesize an evaluation framework to quantify cross-modal semantic relationships that exist between arbitrary pairs of words and images. I study the effectiveness of unsupervised, corpus-based approaches to automatically derive the semantic relatedness between words and images, and perform empirical evaluations by measuring its correlation with human annotators.
Date: December 2012
Creator: Leong, Chee Wee

Source and Channel Coding Strategies for Wireless Sensor Networks

Description: In this dissertation, I focus on source coding techniques as well as channel coding techniques. I addressed the challenges in WSN by developing (1) a new source coding strategy for erasure channels that has better distortion performance compared to MDC; (2) a new cooperative channel coding strategy for multiple access channels that has better channel outage performances compared to MIMO; (3) a new source-channel cooperation strategy to accomplish source-to-fusion center communication that reduces system distortion and improves outage performance. First, I draw a parallel between the 2x2 MDC scheme and the Alamouti's space time block coding (STBC) scheme and observe the commonality in their mathematical models. This commonality allows us to observe the duality between the two diversity techniques. Making use of this duality, I develop an MDC scheme with pairwise complex correlating transform. Theoretically, I show that MDC scheme results in: 1) complete elimination of the estimation error when only one descriptor is received; 2) greater efficiency in recovering the stronger descriptor (with larger variance) from the weaker descriptor; and 3) improved performance in terms of minimized distortion as the quantization error gets reduced. Experiments are also performed on real images to demonstrate these benefits. Second, I present a two-phase cooperative communication strategy and an optimal power allocation strategy to transmit sensor observations to a fusion center in a large-scale sensor network. Outage probability is used to evaluate the performance of the proposed system. Simulation results demonstrate that: 1) when signal-to-noise ratio is low, the performance of the proposed system is better than that of the MIMO system over uncorrelated slow fading Rayleigh channels; 2) given the transmission rate and the total transmission SNR, there exists an optimal power allocation that minimizes the outage probability; 3) on correlated slow fading Rayleigh channels, channel correlation will degrade the system performance ...
Date: December 2012
Creator: Li, Li

Evaluating Appropriateness of Emg and Flex Sensors for Classifying Hand Gestures

Description: Hand and arm gestures are a great way of communication when you don't want to be heard, quieter and often more reliable than whispering into a radio mike. In recent years hand gesture identification became a major active area of research due its use in various applications. The objective of my work is to develop an integrated sensor system, which will enable tactical squads and SWAT teams to communicate when there is absence of a Line of Sight or in the presence of any obstacles. The gesture set involved in this work is the standardized hand signals for close range engagement operations used by military and SWAT teams. The gesture sets involved in this work are broadly divided into finger movements and arm movements. The core components of the integrated sensor system are: Surface EMG sensors, Flex sensors and accelerometers. Surface EMG is the electrical activity produced by muscle contractions and measured by sensors directly attached to the skin. Bend Sensors use a piezo resistive material to detect the bend. The sensor output is determined by both the angle between the ends of the sensor as well as the flex radius. Accelerometers sense the dynamic acceleration and inclination in 3 directions simultaneously. EMG sensors are placed on the upper and lower forearm and assist in the classification of the finger and wrist movements. Bend sensors are mounted on a glove that is worn on the hand. The sensors are located over the first knuckle of each figure and can determine if the finger is bent or not. An accelerometer is attached to the glove at the base of the wrist and determines the speed and direction of the arm movement. Classification algorithm SVM is used to classify the gestures.
Date: May 2013
Creator: Akumalla, Sarath Chandra

Extrapolating Subjectivity Research to Other Languages

Description: Socrates articulated it best, "Speak, so I may see you." Indeed, language represents an invisible probe into the mind. It is the medium through which we express our deepest thoughts, our aspirations, our views, our feelings, our inner reality. From the beginning of artificial intelligence, researchers have sought to impart human like understanding to machines. As much of our language represents a form of self expression, capturing thoughts, beliefs, evaluations, opinions, and emotions which are not available for scrutiny by an outside observer, in the field of natural language, research involving these aspects has crystallized under the name of subjectivity and sentiment analysis. While subjectivity classification labels text as either subjective or objective, sentiment classification further divides subjective text into either positive, negative or neutral. In this thesis, I investigate techniques of generating tools and resources for subjectivity analysis that do not rely on an existing natural language processing infrastructure in a given language. This constraint is motivated by the fact that the vast majority of human languages are scarce from an electronic point of view: they lack basic tools such as part-of-speech taggers, parsers, or basic resources such as electronic text, annotated corpora or lexica. This severely limits the implementation of techniques on par with those developed for English, and by applying methods that are lighter in the usage of text processing infrastructure, we are able to conduct multilingual subjectivity research in these languages as well. Since my aim is also to minimize the amount of manual work required to develop lexica or corpora in these languages, the techniques proposed employ a lever approach, where English often acts as the donor language (the fulcrum in a lever) and allows through a relatively minimal amount of effort to establish preliminary subjectivity research in a target language.
Date: May 2013
Creator: Banea, Carmen