UNT Libraries - 53 Matching Results

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Accurate Joint Detection from Depth Videos towards Pose Analysis

Description: Joint detection is vital for characterizing human pose and serves as a foundation for a wide range of computer vision applications such as physical training, health care, entertainment. This dissertation proposed two methods to detect joints in the human body for pose analysis. The first method detects joints by combining body model and automatic feature points detection together. The human body model maps the detected extreme points to the corresponding body parts of the model and detects the position of implicit joints. The dominant joints are detected after implicit joints and extreme points are located by a shortest path based methods. The main contribution of this work is a hybrid framework to detect joints on the human body to achieve robustness to different body shapes or proportions, pose variations and occlusions. Another contribution of this work is the idea of using geodesic features of the human body to build a model for guiding the human pose detection and estimation. The second proposed method detects joints by segmenting human body into parts first and then detect joints by making the detection algorithm focusing on each limb. The advantage of applying body part segmentation first is that the body segmentation method narrows down the searching area for each joint so that the joint detection method can provide more stable and accurate results.
Date: May 2018
Creator: Kong, Longbo

Application-Specific Things Architectures for IoT-Based Smart Healthcare Solutions

Description: Human body is a complex system organized at different levels such as cells, tissues and organs, which contributes to 11 important organ systems. The functional efficiency of this complex system is evaluated as health. Traditional healthcare is unable to accommodate everyone's need due to the ever-increasing population and medical costs. With advancements in technology and medical research, traditional healthcare applications are shaping into smart healthcare solutions. Smart healthcare helps in continuously monitoring our body parameters, which helps in keeping people health-aware. It provides the ability for remote assistance, which helps in utilizing the available resources to maximum potential. The backbone of smart healthcare solutions is Internet of Things (IoT) which increases the computing capacity of the real-world components by using cloud-based solutions. The basic elements of these IoT based smart healthcare solutions are called "things." Things are simple sensors or actuators, which have the capacity to wirelessly connect with each other and to the internet. The research for this dissertation aims in developing architectures for these things, focusing on IoT-based smart healthcare solutions. The core for this dissertation is to contribute to the research in smart healthcare by identifying applications which can be monitored remotely. For this, application-specific thing architectures were proposed based on monitoring a specific body parameter; monitoring physical health for family and friends; and optimizing the power budget of IoT body sensor network using human body communications. The experimental results show promising scope towards improving the quality of life, through needle-less and cost-effective smart healthcare solutions.
Date: May 2018
Creator: Sundaravadivel, Prabha

Computational Approaches for Analyzing Social Support in Online Health Communities

Description: Online health communities (OHCs) have become a medium for patients to share their personal experiences and interact with peers on topics related to a disease, medication, side effects, and therapeutic processes. Many studies show that using OHCs regularly decreases mortality and improves patients mental health. As a result of their benefits, OHCs are a popular place for patients to refer to, especially patients with a severe disease, and to receive emotional and informational support. The main reasons for developing OHCs are to present valid and high-quality information and to understand the mechanism of social support in changing patients' mental health. Given the purpose of OHC moderators for developing OHCs applications and the purpose of patients for using OHCs, there is no facility, feature, or sub-application in OHCs to satisfy patient and moderator goals. OHCs are only equipped with a primary search engine that is a keyword-based search tool. In other words, if a patient wants to obtain information about a side-effect, he/she needs to browse many threads in the hope that he/she can find several related comments. In the same way, OHC moderators cannot browse all information which is exchanged among patients to validate their accuracy. Thus, it is critical for OHCs to be equipped with computational tools which are supported by several sophisticated computational models that provide moderators and patients with the collection of messages that they need for making decisions or predictions. We present multiple computational models to alleviate the problem of OHCs in providing specific types of messages in response to the specific moderator and patient needs. Specifically, we focused on proposing computational models for the following tasks: identifying emotional support, which presents OHCs moderators, psychologists, and sociologists with insightful views on the emotional states of individuals and groups, and identifying informational support, which provides patients with ...
Date: May 2018
Creator: Khan Pour, Hamed

Hybrid Approaches in Test Suite Prioritization

Description: The rapid advancement of web and mobile application technologies has recently posed numerous challenges to the Software Engineering community, including how to cost-effectively test applications that have complex event spaces. Many software testing techniques attempt to cost-effectively improve the quality of such software. This dissertation primarily focuses on that of hybrid test suite prioritization. The techniques utilize two or more criteria to perform test suite prioritization as it is often insufficient to use only a single criterion. The dissertation consists of the following contributions: (1) a weighted test suite prioritization technique that employs the distance between criteria as a weighting factor, (2) a coarse-to-fine grained test suite prioritization technique that uses a multilevel approach to increase the granularity of the criteria at each subsequent iteration, (3) the Caret-HM tool for Android user session-based testing that allows testers to record, replay, and create heat maps from user interactions with Android applications via a web browser, and (4) Android user session-based test suite prioritization techniques that utilize heuristics developed from user sessions created by Caret-HM. Each of the chapters empirically evaluate the respective techniques. The proposed techniques generally show improved or equally good performance when compared to the baselines, depending on an application under test. Further, this dissertation provides guidance to testers as it relates to the use of the proposed hybrid techniques.
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Date: May 2018
Creator: Nurmuradov, Dmitriy

Validation and Evaluation of Emergency Response Plans through Agent-Based Modeling and Simulation

Description: Biological emergency response planning plays a critical role in protecting the public from possible devastating results of sudden disease outbreaks. These plans describe the distribution of medical countermeasures across a region using limited resources within a restricted time window. Thus, the ability to determine that such a plan will be feasible, i.e. successfully provide service to affected populations within the time limit, is crucial. Many of the current efforts to validate plans are in the form of live drills and training, but those may not test plan activation at the appropriate scale or with sufficient numbers of participants. Thus, this necessitates the use of computational resources to aid emergency managers and planners in developing and evaluating plans before they must be used. Current emergency response plan generation software packages such as RE-PLAN or RealOpt, provide rate-based validation analyses. However, these types of analysis may neglect details of real-world traffic dynamics. Therefore, this dissertation presents Validating Emergency Response Plan Execution Through Simulation (VERPETS), a novel, computational system for the agent-based simulation of biological emergency response plan activation. This system converts raw road network, population distribution, and emergency response plan data into a format suitable for simulation, and then performs these simulations using SUMO, or Simulations of Urban Mobility, to simulate realistic traffic dynamics. Additionally, high performance computing methodologies were utilized to decrease agent load on simulations and improve performance. Further strategies, such as use of agent scaling and a time limit on simulation execution, were also examined. Experimental results indicate that the time to plan completion, i.e. the time when all individuals of the population have received medication, determined by VERPETS aligned well with current alternate methodologies. It was determined that the dynamic of traffic congestion at the POD itself was one of the major factors affecting the completion time of ...
Date: May 2018
Creator: Helsing, Joseph

Analysis and Performance of a Cyber-Human System and Protocols for Geographically Separated Collaborators

Description: This dissertation provides an innovative mechanism to collaborate two geographically separated people on a physical task and a novel method to measure Complexity Index (CI) and calculate Minimal Complexity Index (MCI) of a collaboration protocol. The protocol is represented as a structure, and the information content of it is measured in bits to understand the complex nature of the protocol. Using the complexity metrics, one can analyze the performance of a collaborative system and a collaboration protocol. Security and privacy of the consumers are vital while seeking remote help; this dissertation also provides a novel authorization framework for dynamic access control of resources on an input-constrained appliance used for completing the physical task. Using the innovative Collaborative Appliance for REmote-help (CARE) and with the support of a remotely located expert, fifty-nine subjects with minimal or no prior mechanical knowledge are able to elevate a car for replacing a tire in an average time of six minutes and 53 seconds and with an average protocol complexity of 171.6 bits. Moreover, thirty subjects with minimal or no prior plumbing knowledge are able to change the cartridge of a faucet in an average time of ten minutes and with an average protocol complexity of 250.6 bits. Our experiments and results show that one can use the developed mechanism and methods for expanding the protocols for a variety of home, vehicle, and appliance repairs and installations.
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Date: December 2017
Creator: Jonnada, Srikanth

Evaluation of Call Mobility on Network Productivity in Long Term Evolution Advanced (LTE-A) Femtocells

Description: 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 call blocking vector. The solution to the optimization is maximum network productivity and call arrival rates for all cells for network deployment where peak productivity is identified. We analyze the effects of call mobility on network productivity by simulating low, high, and no mobility scenarios and study the impact based on offered load, handover traffic and blocking probabilities. Finally, we evaluate and optimize performance of fractional frequency reuse (FFR) mechanism and study the impact of proposed metric weighted user satisfaction with sectorized FFR configuration.
Date: December 2017
Creator: Sawant, Uttara

Location Estimation and Geo-Correlated Information Trends

Description: 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 second task, we also propose a Local Event Discovery and Summarization (LEDS) framework to detect local events from Twitter. Many existing algorithms for event detection focus on larger-scale events and are not sensitive to smaller-scale local events. Most of the local events detected by these methods are major events like important sports, shows, or big natural disasters. In this work, we propose the LEDS framework to detect both bigger and smaller events. LEDS contains three key steps: 1) Detecting possible event related terms by monitoring abnormal distribution in different locations and times; 2) Clustering tweets based on their key terms, ...
Date: December 2017
Creator: Liu, Zhi

Online Construction of Android Application Test Suites

Description: Mobile applications play an important role in the dissemination of computing and information resources. They are often used in domains such as mobile banking, e-commerce, and health monitoring. Cost-effective testing techniques in these domains are critical. This dissertation contributes novel techniques for automatic construction of mobile application test suites. In particular, this work provides solutions that focus on the prohibitively large number of possible event sequences that must be sampled in GUI-based mobile applications. This work makes three major contributions: (1) an automated GUI testing tool, Autodroid, that implements a novel online approach to automatic construction of Android application test suites (2) probabilistic and combinatorial-based algorithms that systematically sample the input space of Android applications to generate test suites with GUI/context events and (3) empirical studies to evaluate the cost-effectiveness of our techniques on real-world Android applications. Our experiments show that our techniques achieve better code coverage and event coverage compared to random test generation. We demonstrate that our techniques are useful for automatic construction of Android application test suites in the absence of source code and preexisting abstract models of an Application Under Test (AUT). The insights derived from our empirical studies provide guidance to researchers and practitioners involved in the development of automated GUI testing tools for Android applications.
Date: December 2017
Creator: Adamo Jr., David T

Content and Temporal Analysis of Communications to Predict Task Cohesion in Software Development Global Teams

Description: Virtual teams in industry are increasingly being used to develop software, create products, and accomplish tasks. However, analyzing those collaborations under same-time/different-place conditions is well-known to be difficult. In order to overcome some of these challenges, this research was concerned with the study of collaboration-based, content-based and temporal measures and their ability to predict cohesion within global software development projects. Messages were collected from three software development projects that involved students from two different countries. The similarities and quantities of these interactions were computed and analyzed at individual and group levels. Results of interaction-based metrics showed that the collaboration variables most related to Task Cohesion were Linguistic Style Matching and Information Exchange. The study also found that Information Exchange rate and Reply rate have a significant and positive correlation to Task Cohesion, a factor used to describe participants' engagement in the global software development process. This relation was also found at the Group level. All these results suggest that metrics based on rate can be very useful for predicting cohesion in virtual groups. Similarly, content features based on communication categories were used to improve the identification of Task Cohesion levels. This model showed mixed results, since only Work similarity and Social rate were found to be correlated with Task Cohesion. This result can be explained by how a group's cohesiveness is often associated with fairness and trust, and that these two factors are often achieved by increased social and work communications. Also, at a group-level, all models were found correlated to Task Cohesion, specifically, Similarity+Rate, which suggests that models that include social and work communication categories are also good predictors of team cohesiveness. Finally, temporal interaction similarity measures were calculated to assess their prediction capabilities in a global setting. Results showed a significant negative correlation between the Pacing Rate and ...
Date: May 2017
Creator: Castro Hernandez, Alberto

Probabilistic Analysis of Contracting Ebola Virus Using Contextual Intelligence

Description: The outbreak of the Ebola virus was declared a Public Health Emergency of International Concern by the World Health Organisation (WHO). Due to the complex nature of the outbreak, the Centers for Disease Control and Prevention (CDC) had created interim guidance for monitoring people potentially exposed to Ebola and for evaluating their intended travel and restricting the movements of carriers when needed. Tools to evaluate the risk of individuals and groups of individuals contracting the disease could mitigate the growing anxiety and fear. The goal is to understand and analyze the nature of risk an individual would face when he/she comes in contact with a carrier. This thesis presents a tool that makes use of contextual data intelligence to predict the risk factor of individuals who come in contact with the carrier.
Date: May 2017
Creator: Gopala Krishnan, Arjun

Detection and Classification of Heart Sounds Using a Heart-Mobile Interface

Description: An early detection of heart disease can save lives, caution individuals and also help to determine the type of treatment to be given to the patients. The first test of diagnosing a heart disease is through auscultation - listening to the heart sounds. The interpretation of heart sounds is subjective and requires a professional skill to identify the abnormalities in these sounds. A medical practitioner uses a stethoscope to perform an initial screening by listening for irregular sounds from the patient's chest. Later, echocardiography and electrocardiography tests are taken for further diagnosis. However, these tests are expensive and require specialized technicians to operate. A simple and economical way is vital for monitoring in homecare or rural hospitals and urban clinics. This dissertation is focused on developing a patient-centered device for initial screening of the heart sounds that is both low cost and can be used by the users on themselves, and later share the readings with the healthcare providers. An innovative mobile health service platform is created for analyzing and classifying heart sounds. Certain properties of heart sounds have to be evaluated to identify the irregularities such as the number of heart beats and gallops, intensity, frequency, and duration. Since heart sounds are generated in low frequencies, human ears tend to miss certain sounds as the high frequency sounds mask the lower ones. Therefore, this dissertation provides a solution to process the heart sounds using several signal processing techniques, identifies the features in the heart sounds and finally classifies them. This dissertation enables remote patient monitoring through the integration of advanced wireless communications and a customized low-cost stethoscope. It also permits remote management of patients' cardiac status while maximizing patient mobility. The smartphone application facilities recording, processing, visualizing, listening, and classifying heart sounds. The application also generates an electronic medical ...
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Date: December 2016
Creator: Thiyagaraja, Shanti

Influence of Underlying Random Walk Types in Population Models on Resulting Social Network Types and Epidemiological Dynamics

Description: 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 walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.
Date: December 2016
Creator: Kolgushev, Oleg Mikhailovich

Infusing Automatic Question Generation with Natural Language Understanding

Description: 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 evaluated for question quality and importance via a ranking algorithm.
Date: December 2016
Creator: Mazidi, Karen

Real Time Assessment of a Video Game Player's State of Mind Using Off-the-Shelf Electroencephalography

Description: 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 exit of the state of flow.
Date: December 2016
Creator: McMahan, Timothy

Evaluation Techniques and Graph-Based Algorithms for Automatic Summarization and Keyphrase Extraction

Description: Automatic text summarization and keyphrase extraction are two interesting areas of research which extend along natural language processing and information retrieval. They have recently become very popular because of their wide applicability. Devising generic techniques for these tasks is challenging due to several issues. Yet we have a good number of intelligent systems performing the tasks. As different systems are designed with different perspectives, evaluating their performances with a generic strategy is crucial. It has also become immensely important to evaluate the performances with minimal human effort. In our work, we focus on designing a relativized scale for evaluating different algorithms. This is our major contribution which challenges the traditional approach of working with an absolute scale. We consider the impact of some of the environment variables (length of the document, references, and system-generated outputs) on the performance. Instead of defining some rigid lengths, we show how to adjust to their variations. We prove a mathematically sound baseline that should work for all kinds of documents. We emphasize automatically determining the syntactic well-formedness of the structures (sentences). We also propose defining an equivalence class for each unit (e.g. word) instead of the exact string matching strategy. We show an evaluation approach that considers the weighted relatedness of multiple references to adjust to the degree of disagreements between the gold standards. We publish the proposed approach as a free tool so that other systems can use it. We have also accumulated a dataset (scientific articles) with a reference summary and keyphrases for each document. Our approach is applicable not only for evaluating single-document based tasks but also for evaluating multiple-document based tasks. We have tested our evaluation method for three intrinsic tasks (taken from DUC 2004 conference), and in all three cases, it correlates positively with ROUGE. Based on our experiments ...
Date: August 2016
Creator: Hamid, Fahmida

New Frameworks for Secure Image Communication in the Internet of Things (IoT)

Description: The continuous expansion of technology, broadband connectivity and the wide range of new devices in the IoT cause serious concerns regarding privacy and security. In addition, in the IoT a key challenge is the storage and management of massive data streams. For example, there is always the demand for acceptable size with the highest quality possible for images to meet the rapidly increasing number of multimedia applications. The effort in this dissertation contributes to the resolution of concerns related to the security and compression functions in image communications in the Internet of Thing (IoT), due to the fast of evolution of IoT. This dissertation proposes frameworks for a secure digital camera in the IoT. The objectives of this dissertation are twofold. On the one hand, the proposed framework architecture offers a double-layer of protection: encryption and watermarking that will address all issues related to security, privacy, and digital rights management (DRM) by applying a hardware architecture of the state-of-the-art image compression technique Better Portable Graphics (BPG), which achieves high compression ratio with small size. On the other hand, the proposed framework of SBPG is integrated with the Digital Camera. Thus, the proposed framework of SBPG integrated with SDC is suitable for high performance imaging in the IoT, such as Intelligent Traffic Surveillance (ITS) and Telemedicine. Due to power consumption, which has become a major concern in any portable application, a low-power design of SBPG is proposed to achieve an energy- efficient SBPG design. As the visual quality of the watermarked and compressed images improves with larger values of PSNR, the results show that the proposed SBPG substantially increases the quality of the watermarked compressed images. Higher value of PSNR also shows how robust the algorithm is to different types of attack. From the results obtained for the energy- efficient SBPG ...
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Date: August 2016
Creator: Albalawi, Umar Abdalah S

Sensing and Decoding Brain States for Predicting and Enhancing Human Behavior, Health, and Security

Description: The human brain acts as an intelligent sensor by helping in effective signal communication and execution of logical functions and instructions, thus, coordinating all functions of the human body. More importantly, it shows the potential to combine prior knowledge with adaptive learning, thus ensuring constant improvement. These qualities help the brain to interact efficiently with both, the body (brain-body) as well as the environment (brain-environment). This dissertation attempts to apply the brain-body-environment interactions (BBEI) to elevate human existence and enhance our day-to-day experiences. For instance, when one stepped out of the house in the past, one had to carry keys (for unlocking), money (for purchasing), and a phone (for communication). With the advent of smartphones, this scenario changed completely and today, it is often enough to carry just one's smartphone because all the above activities can be performed with a single device. In the future, with advanced research and progress in BBEI interactions, one will be able to perform many activities by dictating it in one's mind without any physical involvement. This dissertation aims to shift the paradigm of existing brain-computer-interfaces from just ‘control' to ‘monitor, control, enhance, and restore' in three main areas - healthcare, transportation safety, and cryptography. In healthcare, measures were developed for understanding brain-body interactions by correlating cerebral autoregulation with brain signals. The variation in estimated blood flow of brain (obtained through EEG) was detected with evoked change in blood pressure, thus, enabling EEG metrics to be used as a first hand screening tool to check impaired cerebral autoregulation. To enhance road safety, distracted drivers' behavior in various multitasking scenarios while driving was identified by significant changes in the time-frequency spectrum of the EEG signals. A distraction metric was calculated to rank the severity of a distraction task that can be used as an intuitive measure ...
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Date: August 2016
Creator: Bajwa, Garima

Analysis and Optimization of Graphene FET based Nanoelectronic Integrated Circuits

Description: Like cell to the human body, transistors are the basic building blocks of any electronics circuits. Silicon has been the industries obvious choice for making transistors. Transistors with large size occupy large chip area, consume lots of power and the number of functionalities will be limited due to area constraints. Thus to make the devices smaller, smarter and faster, the transistors are aggressively scaled down in each generation. Moore's law states that the transistors count in any electronic circuits doubles every 18 months. Following this Moore's law, the transistor has already been scaled down to 14 nm. However there are limitations to how much further these transistors can be scaled down. Particularly below 10 nm, these silicon based transistors hit the fundamental limits like loss of gate control, high leakage and various other short channel effects. Thus it is not possible to favor the silicon transistors for future electronics applications. As a result, the research has shifted to new device concepts and device materials alternative to silicon. Carbon is the next abundant element found in the Earth and one of such carbon based nanomaterial is graphene. Graphene when extracted from Graphite, the same material used as the lid in pencil, have a tremendous potential to take future electronics devices to new heights in terms of size, cost and efficiency. Thus after its first experimental discovery of graphene in 2004, graphene has been the leading research area for both academics as well as industries. This dissertation is focused on the analysis and optimization of graphene based circuits for future electronics. The first part of this dissertation considers graphene based transistors for analog/radio frequency (RF) circuits. In this section, a dual gate Graphene Field Effect Transistor (GFET) is considered to build the case study circuits like voltage controlled oscillator (VCO) and low ...
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Date: May 2016
Creator: Joshi, Shital

Adaptive Power Management for Autonomic Resource Configuration in Large-scale Computer Systems

Description: In order to run and manage resource-intensive high-performance applications, large-scale computing and storage platforms have been evolving rapidly in various domains in both academia and industry. The energy expenditure consumed to operate and maintain these cloud computing infrastructures is a major factor to influence the overall profit and efficiency for most cloud service providers. Moreover, considering the mitigation of environmental damage from excessive carbon dioxide emission, the amount of power consumed by enterprise-scale data centers should be constrained for protection of the environment.Generally speaking, there exists a trade-off between power consumption and application performance in large-scale computing systems and how to balance these two factors has become an important topic for researchers and engineers in cloud and HPC communities. Therefore, minimizing the power usage while satisfying the Service Level Agreements have become one of the most desirable objectives in cloud computing research and implementation. Since the fundamental feature of the cloud computing platform is hosting workloads with a variety of characteristics in a consolidated and on-demand manner, it is demanding to explore the inherent relationship between power usage and machine configurations. Subsequently, with an understanding of these inherent relationships, researchers are able to develop effective power management policies to optimize productivity by balancing power usage and system performance. In this dissertation, we develop an autonomic power-aware system management framework for large-scale computer systems. We propose a series of techniques including coarse-grain power profiling, VM power modelling, power-aware resource auto-configuration and full-system power usage simulator. These techniques help us to understand the characteristics of power consumption of various system components. Based on these techniques, we are able to test various job scheduling strategies and develop resource management approaches to enhance the systems' power efficiency.
Date: August 2015
Creator: Zhang, Ziming

Advanced Power Amplifiers Design for Modern Wireless Communication

Description: Modern wireless communication systems use spectrally efficient modulation schemes to reach high data rate transmission. These schemes are generally involved with signals with high peak-to-average power ratio (PAPR). Moreover, the development of next generation wireless communication systems requires the power amplifiers to operate over a wide frequency band or multiple frequency bands to support different applications. These wide-band and multi-band solutions will lead to reductions in both the size and cost of the whole system. This dissertation presents several advanced power amplifier solutions to provide wide-band and multi-band operations with efficiency improvement at power back-offs.
Date: August 2015
Creator: Shao, Jin

Predictive Modeling for Persuasive Ambient Technology

Description: Computer scientists are increasingly aware of the power of ubiquitous computing systems that can display information in and about the user's environment. One sub category of ubiquitous computing is persuasive ambient information systems that involve an informative display transitioning between the periphery and center of attention. The goal of this ambient technology is to produce a behavior change, implying that a display must be informative, unobtrusive, and persuasive. While a significant body of research exists on ambient technology, previous research has not fully explored the different measures to identify behavior change, evaluation techniques for linking design characteristics to visual effectiveness, nor the use of short-term goals to affect long-term behavior change. This study uses the unique context of noise-induced hearing loss (NIHL) among collegiate musicians to explore these issues through developing the MIHL Reduction Feedback System that collects real-time data, translates it into visuals for music classrooms, provides predictive outcomes for goalsetting persuasion, and provides statistical measures of behavior change.
Date: August 2015
Creator: Powell, Jason W.

The Procedural Generation of Interesting Sokoban Levels

Description: As video games continue to become larger, more complex, and more costly to produce, research into methods to make game creation easier and faster becomes more valuable. One such research topic is procedural generation, which allows the computer to assist in the creation of content. This dissertation presents a new algorithm for the generation of Sokoban levels. Sokoban is a grid-based transport puzzle which is computational interesting due to being PSPACE-complete. Beyond just generating levels, the question of whether or not the levels created by this algorithm are interesting to human players is explored. A study was carried out comparing player attention while playing hand made levels versus their attention during procedurally generated levels. An auditory Stroop test was used to measure attention without disrupting play.
Date: May 2015
Creator: Taylor, Joshua

Space and Spectrum Engineered High Frequency Components and Circuits

Description: With the increasing demand on wireless and portable devices, the radio frequency front end blocks are required to feature properties such as wideband, high frequency, multiple operating frequencies, low cost and compact size. However, the current radio frequency system blocks are designed by combining several individual frequency band blocks into one functional block, which increase the cost and size of devices. To address these issues, it is important to develop novel approaches to further advance the current design methodologies in both space and spectrum domains. In recent years, the concept of artificial materials has been proposed and studied intensively in RF/Microwave, Terahertz, and optical frequency range. It is a combination of conventional materials such as air, wood, metal and plastic. It can achieve the material properties that have not been found in nature. Therefore, the artificial material (i.e. meta-materials) provides design freedoms to control both the spectrum performance and geometrical structures of radio frequency front end blocks and other high frequency systems. In this dissertation, several artificial materials are proposed and designed by different methods, and their applications to different high frequency components and circuits are studied. First, quasi-conformal mapping (QCM) method is applied to design plasmonic wave-adapters and couplers working at the optical frequency range. Second, inverse QCM method is proposed to implement flattened Luneburg lens antennas and parabolic antennas in the microwave range. Third, a dual-band compact directional coupler is realized by applying artificial transmission lines. In addition, a fully symmetrical coupler with artificial lumped element structure is also implemented. Finally, a tunable on-chip inductor, compact CMOS transmission lines, and metamaterial-based interconnects are proposed using artificial metal structures. All the proposed designs are simulated in full-wave 3D electromagnetic solvers, and the measurement results agree well with the simulation results. These artificial material-based novel design methodologies pave the way ...
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Date: May 2015
Creator: Arigong, Bayaner