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Web-based airline ticket booking system.

Description: Online airline ticket booking system is one of the essential applications of E-commerce. With the development of Internet and security technology, more and more people begin to consume online, which is more convenient and personal than traditional way. The goal of this system is to make people purchase airline tickets easily. The system is written in JAVATM. Chapter 1 will introduce some basic conception of the technologies have been used in this system. Chapter 2 shows how the database and the system are designed. Chapter 3 shows the logic of the Web site. In Chapter 4 the interface of the system will be given. Chapter 5 tells the platform of this system.
Date: August 2004
Creator: Yu, Jianming

Exploring Analog and Digital Design Using the Open-Source Electric VLSI Design System

Description: The design of VLSI electronic circuits can be achieved at many different abstraction levels starting from system behavior to the most detailed, physical layout level. As the number of transistors in VLSI circuits is increasing, the complexity of the design is also increasing, and it is now beyond human ability to manage. Hence CAD (Computer Aided design) or EDA (Electronic Design Automation) tools are involved in the design. EDA or CAD tools automate the design, verification and testing of these VLSI circuits. In today’s market, there are many EDA tools available. However, they are very expensive and require high-performance platforms. One of the key challenges today is to select appropriate CAD or EDA tools which are open-source for academic purposes. This thesis provides a detailed examination of an open-source EDA tool called Electric VLSI Design system. An excellent and efficient CAD tool useful for students and teachers to implement ideas by modifying the source code, Electric fulfills these requirements. This thesis' primary objective is to explain the Electric software features and architecture and to provide various digital and analog designs that are implemented by this software for educational purposes. Since the choice of an EDA tool is based on the efficiency and functions that it can provide, this thesis explains all the analysis and synthesis tools that electric provides and how efficient they are. Hence, this thesis is of benefit for students and teachers that choose Electric as their open-source EDA tool for educational purposes.
Date: May 2016
Creator: Aluru, Gunasekhar

An Empirical Study of How Novice Programmers Use the Web

Description: Students often use the web as a source of help for problems that they encounter on programming assignments.In this work, we seek to understand how students use the web to search for help on their assignments.We used a mixed methods approach with 344 students who complete a survey and 41 students who participate in a focus group meetings and helped in recording data about their search habits.The survey reveals data about student reported search habits while the focus group uses a web browser plug-in to record actual search patterns.We examine the results collectively and as broken down by class year.Survey results show that at least 2/3 of the students from each class year rely on search engines to locate resources for help with their programming bugs in at least half of their assignments;search habits vary by class year;and the value of different types of resources such as tutorials and forums varies by class year.Focus group results exposes the high frequency web sites used by the students in solving their programming assignments.
Date: May 2016
Creator: Tula, Naveen

Simulink(R) Based Design and Implementation of a Solar Power Based Mobile Charger

Description: Electrical energy is used at approximately the rate of 15 Terawatts world-wide. Generating this much energy has become a primary concern for all nations. There are many ways of generating energy among which the most commonly used are non-renewable and will extinct much sooner than expected. Very active research is going on both to increase the use of renewable energy sources and to use the available energy with more efficiency. Among these sources, solar energy is being considered as the most abundant and has received high attention. The mobile phone has become one of the basic needs of modern life, with almost every human being having one.Individually a mobile phone consumes little power but collectively this becomes very large. This consideration motivated the research undertaken in this masters thesis. The objective of this thesis is to design a model for solar power based charging circuits for mobile phone using Simulink(R). This thesis explains a design procedure of solar power based mobile charger circuit using Simulink(R) which includes the models for the photo-voltaic array, maximum power point tracker, pulse width modulator, DC-DC converter and a battery.The first part of the thesis concentrates on electron level behavior of a solar cell, its structure and its electrical model.The second part is to design an array of solar cells to generate the desired output.Finally, the third part is to design a DC-DC converter which can stabilize and provide the required input to the battery with the help of the maximum power point tracker and pulse width modulation.The obtained DC-DC converter is adjustable to meet the requirements of the battery. This design is aimed at charging a lithium ion battery with nominal voltage of 3.7 V, which can be taken as baseline to charge different types of batteries with different nominal voltages.
Date: May 2016
Creator: Mukka, Manoj Kumar

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

The Role of Intelligent Mobile Agents in Network Management and Routing

Description: 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.
Date: December 2000
Creator: Balamuru, Vinay Gopal

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

Simulink Based Modeling of a Multi Global Navigation Satellite System

Description: 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.
Date: December 2016
Creator: Mukka, Nagaraju

Classifying Pairwise Object Interactions: A Trajectory Analytics Approach

Description: We have a huge amount of video data from extensively available surveillance cameras and increasingly growing technology to record the motion of a moving object in the form of trajectory data. With proliferation of location-enabled devices and ongoing growth in smartphone penetration as well as advancements in exploiting image processing techniques, tracking moving objects is more flawlessly achievable. In this work, we explore some domain-independent qualitative and quantitative features in raw trajectory (spatio-temporal) data in videos captured by a fixed single wide-angle view camera sensor in outdoor areas. We study the efficacy of those features in classifying four basic high level actions by employing two supervised learning algorithms and show how each of the features affect the learning algorithms’ overall accuracy as a single factor or confounded with others.
Date: May 2015
Creator: Janmohammadi, Siamak

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

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

Distributed Frameworks Towards Building an Open Data Architecture

Description: Data is everywhere. The current Technological advancements in Digital, Social media and the ease at which the availability of different application services to interact with variety of systems are causing to generate tremendous volumes of data. Due to such varied services, Data format is now not restricted to only structure type like text but can generate unstructured content like social media data, videos and images etc. The generated Data is of no use unless been stored and analyzed to derive some Value. Traditional Database systems comes with limitations on the type of data format schema, access rates and storage sizes etc. Hadoop is an Apache open source distributed framework that support storing huge datasets of different formatted data reliably on its file system named Hadoop File System (HDFS) and to process the data stored on HDFS using MapReduce programming model. This thesis study is about building a Data Architecture using Hadoop and its related open source distributed frameworks to support a Data flow pipeline on a low commodity hardware. The Data flow components are, sourcing data, storage management on HDFS and data access layer. This study also discuss about a use case to utilize the architecture components. Sqoop, a framework to ingest the structured data from database onto Hadoop and Flume is used to ingest the semi-structured Twitter streaming json data on to HDFS for analysis. The data sourced using Sqoop and Flume have been analyzed using Hive for SQL like analytics and at a higher level of data access layer, Hadoop has been compared with an in memory computing system using Spark. Significant differences in query execution performances have been analyzed when working with Hadoop and Spark frameworks. This integration helps for ingesting huge Volumes of streaming json Variety data to derive better Value based analytics using Hive and ...
Date: May 2015
Creator: Venumuddala, Ramu Reddy

Towards Resistance Detection in Health Behavior Change Dialogue Systems

Description: One of the challenges fairly common in motivational interviewing is patient resistance to health behavior change. Hence, automated dialog systems aimed at counseling patients need to be capable of detecting resistance and appropriately altering dialog. This thesis focusses primarily on the development of such a system for automatic identification of patient resistance to behavioral change. This enables the dialogue system to direct the discourse towards a more agreeable ground and helping the patient overcome the obstacles in his or her way to change. This thesis also proposes a dialogue system framework for health behavior change via natural language analysis and generation. The proposed framework facilitates automated motivational interviewing from clinical psychology and involves three broad stages: rapport building and health topic identification, assessment of the patient’s opinion about making a change, and developing a plan. Using this framework patients can be encouraged to reflect on the options available and choose the best for a healthier life.
Date: August 2015
Creator: Sarma, Bandita

Freeform Cursive Handwriting Recognition Using a Clustered Neural Network

Description: Optical character recognition (OCR) software has advanced greatly in recent years. Machine-printed text can be scanned and converted to searchable text with word accuracy rates around 98%. Reasonably neat hand-printed text can be recognized with about 85% word accuracy. However, cursive handwriting still remains a challenge, with state-of-the-art performance still around 75%. Algorithms based on hidden Markov models have been only moderately successful, while recurrent neural networks have delivered the best results to date. This thesis explored the feasibility of using a special type of feedforward neural network to convert freeform cursive handwriting to searchable text. The hidden nodes in this network were grouped into clusters, with each cluster being trained to recognize a unique character bigram. The network was trained on writing samples that were pre-segmented and annotated. Post-processing was facilitated in part by using the network to identify overlapping bigrams that were then linked together to form words and sentences. With dictionary assisted post-processing, the network achieved word accuracy of 66.5% on a small, proprietary corpus. The contributions in this thesis are threefold: 1) the novel clustered architecture of the feed-forward neural network, 2) the development of an expanded set of observers combining image masks, modifiers, and feature characterizations, and 3) the use of overlapping bigrams as the textual working unit to assist in context analysis and reconstruction.
Date: August 2015
Creator: Bristow, Kelly H.

Uncertainty Evaluation in Large-scale Dynamical Systems: Theory and Applications

Description: Significant research efforts have been devoted to large-scale dynamical systems, with the aim of understanding their complicated behaviors and managing their responses in real-time. One pivotal technological obstacle in this process is the existence of uncertainty. Although many of these large-scale dynamical systems function well in the design stage, they may easily fail when operating in realistic environment, where environmental uncertainties modulate system dynamics and complicate real-time predication and management tasks. This dissertation aims to develop systematic methodologies to evaluate the performance of large-scale dynamical systems under uncertainty, as a step toward real-time decision support. Two uncertainty evaluation approaches are pursued: the analytical approach and the effective simulation approach. The analytical approach abstracts the dynamics of original stochastic systems, and develops tractable analysis (e.g., jump-linear analysis) for the approximated systems. Despite the potential bias introduced in the approximation process, the analytical approach provides rich insights valuable for evaluating and managing the performance of large-scale dynamical systems under uncertainty. When a system’s complexity and scale are beyond tractable analysis, the effective simulation approach becomes very useful. The effective simulation approach aims to use a few smartly selected simulations to quickly evaluate a complex system’s statistical performance. This approach was originally developed to evaluate a single uncertain variable. This dissertation extends the approach to be scalable and effective for evaluating large-scale systems under a large-number of uncertain variables. While a large portion of this dissertation focuses on the development of generic methods and theoretical analysis that are applicable to broad large-scale dynamical systems, many results are illustrated through a representative large-scale system application on strategic air traffic management application, which is concerned with designing robust management plans subject to a wide range of weather possibilities at 2-15 hours look-ahead time.
Date: December 2014
Creator: Zhou, Yi (Software engineer)

Unique Channel Email System

Description: Email connects 85% of the world. This paper explores the pattern of information overload encountered by majority of email users and examine what steps key email providers are taking to combat the problem. Besides fighting spam, popular email providers offer very limited tools to reduce the amount of unwanted incoming email. Rather, there has been a trend to expand storage space and aid the organization of email. Storing email is very costly and harmful to the environment. Additionally, information overload can be detrimental to productivity. We propose a simple solution that results in drastic reduction of unwanted mail, also known as graymail.
Date: August 2015
Creator: Balakchiev, Milko

Investigation on Segmentation, Recognition and 3D Reconstruction of Objects Based on LiDAR Data Or MRI

Description: Segmentation, recognition and 3D reconstruction of objects have been cutting-edge research topics, which have many applications ranging from environmental and medical to geographical applications as well as intelligent transportation. In this dissertation, I focus on the study of segmentation, recognition and 3D reconstruction of objects using LiDAR data/MRI. Three main works are that (I). Feature extraction algorithm based on sparse LiDAR data. A novel method has been proposed for feature extraction from sparse LiDAR data. The algorithm and the related principles have been described. Also, I have tested and discussed the choices and roles of parameters. By using correlation of neighboring points directly, statistic distribution of normal vectors at each point has been effectively used to determine the category of the selected point. (II). Segmentation and 3D reconstruction of objects based on LiDAR/MRI. The proposed method includes that the 3D LiDAR data are layered, that different categories are segmented, and that 3D canopy surfaces of individual tree crowns and clusters of trees are reconstructed from LiDAR point data based on a region active contour model. The proposed method allows for delineations of 3D forest canopy naturally from the contours of raw LiDAR point clouds. The proposed model is suitable not only for a series of ideal cone shapes, but also for other kinds of 3D shapes as well as other kinds dataset such as MRI. (III). Novel algorithms for recognition of objects based on LiDAR/MRI. Aimed to the sparse LiDAR data, the feature extraction algorithm has been proposed and applied to classify the building and trees. More importantly, the novel algorithms based on level set methods have been provided and employed to recognize not only the buildings and trees, the different trees (e.g. Oak trees and Douglas firs), but also the subthalamus nuclei (STNs). By using the novel algorithms based ...
Date: May 2015
Creator: Tang, Shijun

Integrity Verification of Applications on RADIUM Architecture

Description: Trusted Computing capability has become ubiquitous these days, and it is being widely deployed into consumer devices as well as enterprise platforms. As the number of threats is increasing at an exponential rate, it is becoming a daunting task to secure the systems against them. In this context, the software integrity measurement at runtime with the support of trusted platforms can be a better security strategy. Trusted Computing devices like TPM secure the evidence of a breach or an attack. These devices remain tamper proof if the hardware platform is physically secured. This type of trusted security is crucial for forensic analysis in the aftermath of a breach. The advantages of trusted platforms can be further leveraged if they can be used wisely. RADIUM (Race-free on-demand Integrity Measurement Architecture) is one such architecture, which is built on the strength of TPM. RADIUM provides an asynchronous root of trust to overcome the TOC condition of DRTM. Even though the underlying architecture is trusted, attacks can still compromise applications during runtime by exploiting their vulnerabilities. I propose an application-level integrity measurement solution that fits into RADIUM, to expand the trusted computing capability to the application layer. This is based on the concept of program invariants that can be used to learn the correct behavior of an application. I used Daikon, a tool to obtain dynamic likely invariants, and developed a method of observing these properties at runtime to verify the integrity. The integrity measurement component was implemented as a Python module on top of Volatility, a virtual machine introspection tool. My approach is a first step towards integrity attestation, using hypervisor-based introspection on RADIUM and a proof of concept of application-level measurement capability.
Date: August 2015
Creator: Tarigopula, Mohan Krishna

Radium: Secure Policy Engine in Hypervisor

Description: The basis of today’s security systems is the trust and confidence that the system will behave as expected and are in a known good trusted state. The trust is built from hardware and software elements that generates a chain of trust that originates from a trusted known entity. Leveraging hardware, software and a mandatory access control policy technology is needed to create a trusted measurement environment. Employing a control layer (hypervisor or microkernel) with the ability to enforce a fine grained access control policy with hyper call granularity across multiple guest virtual domains can ensure that any malicious environment to be contained. In my research, I propose the use of radium's Asynchronous Root of Trust Measurement (ARTM) capability incorporated with a secure mandatory access control policy engine that would mitigate the limitations of the current hardware TPM solutions. By employing ARTM we can leverage asynchronous use of boot, launch, and use with the hypervisor proving its state and the integrity of the secure policy. My solution is using Radium (Race free on demand integrity architecture) architecture that will allow a more detailed measurement of applications at run time with greater semantic knowledge of the measured environments. Radium incorporation of a secure access control policy engine will give it the ability to limit or empower a virtual domain system. It can also enable the creation of a service oriented model of guest virtual domains that have the ability to perform certain operations such as introspecting other virtual domain systems to determine the integrity or system state and report it to a remote entity.
Date: August 2015
Creator: Shah, Tawfiq M.

Automatic Removal of Complex Shadows From Indoor Videos

Description: Shadows in indoor scenarios are usually characterized with multiple light sources that produce complex shadow patterns of a single object. Without removing shadow, the foreground object tends to be erroneously segmented. The inconsistent hue and intensity of shadows make automatic removal a challenging task. In this thesis, a dynamic thresholding and transfer learning-based method for removing shadows is proposed. The method suppresses light shadows with a dynamically computed threshold and removes dark shadows using an online learning strategy that is built upon a base classifier trained with manually annotated examples and refined with the automatically identified examples in the new videos. Experimental results demonstrate that despite variation of lighting conditions in videos our proposed method is able to adapt to the videos and remove shadows effectively. The sensitivity of shadow detection changes slightly with different confidence levels used in example selection for classifier retraining and high confidence level usually yields better performance with less retraining iterations.
Date: August 2015
Creator: Mohapatra, Deepankar

Computational Methods for Discovering and Analyzing Causal Relationships in Health Data

Description: Publicly available datasets in health science are often large and observational, in contrast to experimental datasets where a small number of data are collected in controlled experiments. Variables' causal relationships in the observational dataset are yet to be determined. However, there is a significant interest in health science to discover and analyze causal relationships from health data since identified causal relationships will greatly facilitate medical professionals to prevent diseases or to mitigate the negative effects of the disease. Recent advances in Computer Science, particularly in Bayesian networks, has initiated a renewed interest for causality research. Causal relationships can be possibly discovered through learning the network structures from data. However, the number of candidate graphs grows in a more than exponential rate with the increase of variables. Exact learning for obtaining the optimal structure is thus computationally infeasible in practice. As a result, heuristic approaches are imperative to alleviate the difficulty of computations. This research provides effective and efficient learning tools for local causal discoveries and novel methods of learning causal structures with a combination of background knowledge. Specifically in the direction of constraint based structural learning, polynomial-time algorithms for constructing causal structures are designed with first-order conditional independence. Algorithms of efficiently discovering non-causal factors are developed and proved. In addition, when the background knowledge is partially known, methods of graph decomposition are provided so as to reduce the number of conditioned variables. Experiments on both synthetic data and real epidemiological data indicate the provided methods are applicable to large-scale datasets and scalable for causal analysis in health data. Followed by the research methods and experiments, this dissertation gives thoughtful discussions on the reliability of causal discoveries computational health science research, complexity, and implications in health science research.
Date: August 2015
Creator: Liang, Yiheng

Computational Methods for Vulnerability Analysis and Resource Allocation in Public Health Emergencies

Description: POD (Point of Dispensing)-based emergency response plans involving mass prophylaxis may seem feasible when considering the choice of dispensing points within a region, overall population density, and estimated traffic demands. However, the plan may fail to serve particular vulnerable sub-populations, resulting in access disparities during emergency response. Federal authorities emphasize on the need to identify sub-populations that cannot avail regular services during an emergency due to their special needs to ensure effective response. Vulnerable individuals require the targeted allocation of appropriate resources to serve their special needs. Devising schemes to address the needs of vulnerable sub-populations is essential for the effectiveness of response plans. This research focuses on data-driven computational methods to quantify and address vulnerabilities in response plans that require the allocation of targeted resources. Data-driven methods to identify and quantify vulnerabilities in response plans are developed as part of this research. Addressing vulnerabilities requires the targeted allocation of appropriate resources to PODs. The problem of resource allocation to PODs during public health emergencies is introduced and the variants of the resource allocation problem such as the spatial allocation, spatio-temporal allocation and optimal resource subset variants are formulated. Generating optimal resource allocation and scheduling solutions can be computationally hard problems. The application of metaheuristic techniques to find near-optimal solutions to the resource allocation problem in response plans is investigated. A vulnerability analysis and resource allocation framework that facilitates the demographic analysis of population data in the context of response plans, and the optimal allocation of resources with respect to the analysis are described.
Date: August 2015
Creator: Indrakanti, Saratchandra

Cuff-less Blood Pressure Measurement Using a Smart Phone

Description: Blood pressure is vital sign information that physicians often need as preliminary data for immediate intervention during emergency situations or for regular monitoring of people with cardiovascular diseases. Despite the availability of portable blood pressure meters in the market, they are not regularly carried by people, creating a need for an ultra-portable measurement platform or device that can be easily carried and used at all times. One such device is the smartphone which, according to comScore survey is used by 26.2% of the US adult population. the mass production of these phones with built-in sensors and high computation power has created numerous possibilities for application development in different domains including biomedical. Motivated by this capability and their extensive usage, this thesis focuses on developing a blood pressure measurement platform on smartphones. Specifically, I developed a blood pressure measurement system on a smart phone using the built-in camera and a customized external microphone. the system consists of first obtaining heart beats using the microphone and finger pulse with the camera, and finally calculating the blood pressure using the recorded data. I developed techniques for finding the best location for obtaining the data, making the system usable by all categories of people. the proposed system resulted in accuracies between 90-100%, when compared to traditional blood pressure meters. the second part of this thesis presents a new system for remote heart beat monitoring using the smart phone. with the proposed system, heart beats can be transferred live by patients and monitored by physicians remotely for diagnosis. the proposed blood pressure measurement and remote monitoring systems will be able to facilitate information acquisition and decision making by the 9-1-1 operators.
Date: May 2012
Creator: Jonnada, Srikanth

A Driver, Vehicle and Road Safety System Using Smartphones

Description: As vehicle manufacturers continue to increase their emphasis on safety with advanced driver assistance systems (ADAS), I propose a ubiquitous device that is able to analyze and advise on safety conditions. Mobile smartphones are increasing in popularity among younger generations with an estimated 64% of 25-34 year olds already using one in their daily lives. with over 10 million car accidents reported in the United States each year, car manufacturers have shifted their focus of a passive approach (airbags) to more active by adding features associated with ADAS (lane departure warnings). However, vehicles manufactured with these sensors are not economically priced while older vehicles might only have passive safety features. Given its accessibility and portability, I target a mobile smartphone as a device to compliment ADAS that can bring a driver assist to any vehicle without regards for any on-vehicle communication system requirements. I use the 3-axis accelerometer of multiple Android based smartphone to record and analyze various safety factors which can influence a driver while operating a vehicle. These influences with respect to the driver, vehicle and road are lane change maneuvers, vehicular comfort and road conditions. Each factor could potentially be hazardous to the health of the driver, neighboring public, and automobile and is therefore analyzed thoroughly achieving 85.60% and 89.89% classification accuracy for identifying road anomalies and lane changes, respectively. Effective use of this data can educate a potentially dangerous driver on how to operate a vehicle safely and efficiently. with real time analysis and auditory alerts of these factors, I hope to increase a driver's overall awareness to maximize safety.
Date: May 2012
Creator: Gozick, Brandon