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Description
Smart home system (SHS) is a kind of information system aiming at realizing home automation. The SHS can connect with almost any kind of electronic/electric device used in a home so that they can be controlled and monitored centrally. Today's technology also allows the home owners to control and monitor

Smart home system (SHS) is a kind of information system aiming at realizing home automation. The SHS can connect with almost any kind of electronic/electric device used in a home so that they can be controlled and monitored centrally. Today's technology also allows the home owners to control and monitor the SHS installed in their homes remotely. This is typically realized by giving the SHS network access ability. Although the SHS's network access ability brings a lot of conveniences to the home owners, it also makes the SHS facing more security threats than ever before. As a result, when designing a SHS, the security threats it might face should be given careful considerations. System security threats can be solved properly by understanding them and knowing the parts in the system that should be protected against them first. This leads to the idea of solving the security threats a SHS might face from the requirements engineering level. Following this idea, this paper proposes a systematic approach to generate the security requirements specifications for the SHS. It can be viewed as the first step toward the complete SHS security requirements engineering process.
ContributorsXu, Rongcao (Author) / Ghazarian, Arbi (Thesis advisor) / Bansal, Ajay (Committee member) / Lindquist, Timothy (Committee member) / Arizona State University (Publisher)
Created2013
Description
The world’s population is currently 9% visually impaired. Medical sciences do not have a biological fix that can cure this visual impairment. Visually impaired people are currently being assisted with biological fixes or assistive devices. The current assistive devices are limited in size as well as resolution. This thesis presents

The world’s population is currently 9% visually impaired. Medical sciences do not have a biological fix that can cure this visual impairment. Visually impaired people are currently being assisted with biological fixes or assistive devices. The current assistive devices are limited in size as well as resolution. This thesis presents the development and experimental validation of a control system for a new vibrotactile haptic display that is currently in development. In order to allow the vibrotactile haptic display to be used to represent motion, the control system must be able to change the image displayed at a rate of at least 30 frames/second. In order to achieve this, this thesis introduces and investigates the use of three improvements: threading, change filtering, and wave libraries. Through these methods, it is determined that an average of 40 frames/second can be achieved.
ContributorsKIM, KENDRA (Author) / Sodemann, Angela (Thesis advisor) / Robertson, John (Committee member) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their target graph, however they struggle to implement a correct, and

Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their target graph, however they struggle to implement a correct, and efficient, search over that graph.

To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
ContributorsHenderson, Christopher (Author) / Bansal, Ajay (Thesis advisor) / Lindquist, Timothy (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Image processing has changed the way we store, view and share images. One important component of sharing images over the networks is image compression. Lossy image compression techniques compromise the quality of images to reduce their size. To ensure that the distortion of images due to image compression is not

Image processing has changed the way we store, view and share images. One important component of sharing images over the networks is image compression. Lossy image compression techniques compromise the quality of images to reduce their size. To ensure that the distortion of images due to image compression is not highly detectable by humans, the perceived quality of an image needs to be maintained over a certain threshold. Determining this threshold is best done using human subjects, but that is impractical in real-world scenarios. As a solution to this issue, image quality assessment (IQA) algorithms are used to automatically compute a fidelity score of an image.

However, poor performance of IQA algorithms has been observed due to complex statistical computations involved. General Purpose Graphics Processing Unit (GPGPU) programming is one of the solutions proposed to optimize the performance of these algorithms.

This thesis presents a Compute Unified Device Architecture (CUDA) based optimized implementation of full reference IQA algorithm, Visual Signal to Noise Ratio (VSNR) that uses M-level 2D Discrete Wavelet Transform (DWT) with 9/7 biorthogonal filters among other statistical computations. The presented implementation is tested upon four different image quality databases containing images with multiple distortions and sizes ranging from 512 x 512 to 1600 x 1280. The CUDA implementation of VSNR shows a speedup of over 32x for 1600 x 1280 images. It is observed that the speedup scales with the increase in size of images. The results showed that the implementation is fast enough to use VSNR on high definition videos with a frame rate of 60 fps. This work presents the optimizations made due to the use of GPU’s constant memory and reuse of allocated memory on the GPU. Also, it shows the performance improvement using profiler driven GPGPU development in CUDA. The presented implementation can be deployed in production combined with existing applications.
ContributorsGupta, Ayush (Author) / Sohoni, Sohum (Thesis advisor) / Amresh, Ashish (Committee member) / Bansal, Ajay (Committee member) / Arizona State University (Publisher)
Created2017
Description
There exists extensive research on the use of twisty puzzles, such as the Rubik's Cube, in educational contexts to assist in developing critical thinking skills and in teaching abstract concepts, such as group theory. However, the existing research does not consider the use of twisty puzzles in developing language proficiency.

There exists extensive research on the use of twisty puzzles, such as the Rubik's Cube, in educational contexts to assist in developing critical thinking skills and in teaching abstract concepts, such as group theory. However, the existing research does not consider the use of twisty puzzles in developing language proficiency. Furthermore, there remain methodological issues in integrating standard twisty puzzles into a class curriculum due to the ease with which erroneous cube twists occur, leading to a puzzle scramble that deviates from the intended teaching goal. To address these issues, an extensive examination of the "smart cube" market took place in order to determine whether a device that virtualizes twisty puzzles while maintaining the intuitive tactility of manipulating such puzzles can be employed both to fill the language education void and to mitigate the potential frustration experienced by students who unintentionally scramble a puzzle due to executing the wrong moves. This examination revealed the presence of Bluetooth smart cubes, which are capable of interfacing with a companion web or mobile application that visualizes and reacts to puzzle manipulations. This examination also revealed the presence of a device called the WOWCube, which is a 2x2x2 smart cube entertainment system that has 24 Liquid Crystal Display (LCD) screens, one for each face's square, enabling better integration of the application with the puzzle hardware. Developing applications both for the Bluetooth smart cube using React Native and for the WOWCube demonstrated the higher feasibility of developing with the WOWCube due to its streamlined development kit as well as its ability to tie the application to the device hardware, enhancing the tactile immersion of the players with the application itself. Using the WOWCube, a word puzzle game featuring three game modes was implemented to assist in teaching players English vocabulary. Due to its incorporation of features that enable dynamic puzzle generation and resetting, players who participated in a user survey found that the game was compelling and that it exercised their critical thinking skills. This demonstrates the feasibility of smart cube applications in both critical thinking and language skills.
ContributorsHreshchyshyn, Jacob (Author) / Bansal, Ajay (Thesis advisor) / Mehlhase, Alexandra (Committee member) / Baron, Tyler (Committee member) / Arizona State University (Publisher)
Created2023
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Description

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations from Yahoo! Finance’s “Conversations” feature were parsed through the Valence

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations from Yahoo! Finance’s “Conversations” feature were parsed through the Valence Aware Dictionary for Sentiment Reasoning (VADER) natural language processing package to determine numerical polarities which represented positivity or negativity for a given stock ticker. These generated polarities were paired with stock metrics typically observed by stock analysts as the feature set for a Logistic Regression machine learning model. The model was trained on roughly 1500 major stocks to determine a binary classification between a “Buy” or “Not Buy” rating for each stock, and the results of the model were inserted into the back-end of the Agora Web UI which emulates search engine behavior specifically for stocks found in NYSE and NASDAQ. The model reported an accuracy of 82.5% and for most major stocks, the model’s prediction correlated with stock analysts’ ratings. Given the volatility of the stock market and the propensity for hive-mind behavior in online forums, the performance of the Logistic Regression model would benefit from incorporating historical stock data and more sources of opinion to balance any subjectivity in the model.

ContributorsRamaraju, Venkat (Author) / Rao, Jayanth (Co-author) / Bansal, Ajay (Thesis director) / Smith, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2021-12
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Description

User interface development on iOS is in a major transitionary state as Apple introduces a declarative and interactive framework called SwiftUI. SwiftUI’s success depends on how well it integrates its new tooling for novice developers. This paper will demonstrate and discuss where SwiftUI succeeds and fails at carving a new

User interface development on iOS is in a major transitionary state as Apple introduces a declarative and interactive framework called SwiftUI. SwiftUI’s success depends on how well it integrates its new tooling for novice developers. This paper will demonstrate and discuss where SwiftUI succeeds and fails at carving a new path for user interface development for new developers. This is done by comparisons against its existing imperative UI framework UIKit as well as elaborating on the background of SwiftUI and examples of how SwiftUI works to help developers. The paper will also discuss what exactly led to SwiftUI and how it is currently faring on Apple's latest operating systems. SwiftUI is a framework growing and evolving to serve the needs of 5 very different platforms with code that claims to be simpler to write and easier to deploy. The world of UI programming in iOS has been dominated by a Storyboard canvas for years, but SwiftUI claims to link this graphic-first development process with the code programmers are used to by keeping them side by side in constant sync. This bold move requires interactive programming capable of recompilation on the fly. As this paper will discuss, SwiftUI has garnered a community of developers giving it the main property it needs to succeed: a component library.

ContributorsGilchrist, Ethan (Author) / Bansal, Ajay (Thesis director) / Balasooriya, Janaka (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2021-12
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Description

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations from Yahoo! Finance’s “Conversations” feature were parsed through the Valence

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations from Yahoo! Finance’s “Conversations” feature were parsed through the Valence Aware Dictionary for Sentiment Reasoning (VADER) natural language processing package to determine numerical polarities which represented positivity or negativity for a given stock ticker. These generated polarities were paired with stock metrics typically observed by stock analysts as the feature set for a Logistic Regression machine learning model. The model was trained on roughly 1500 major stocks to determine a binary classification between a “Buy” or “Not Buy” rating for each stock, and the results of the model were inserted into the back-end of the Agora Web UI which emulates search engine behavior specifically for stocks found in NYSE and NASDAQ. The model reported an accuracy of 82.5% and for most major stocks, the model’s prediction correlated with stock analysts’ ratings. Given the volatility of the stock market and the propensity for hive-mind behavior in online forums, the performance of the Logistic Regression model would benefit from incorporating historical stock data and more sources of opinion to balance any subjectivity in the model.

ContributorsRao, Jayanth (Author) / Ramaraju, Venkat (Co-author) / Bansal, Ajay (Thesis director) / Smith, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2021-12
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Description
Recommendation systems provide recommendations based on user behavior andcontent data. User behavior and content data are fed to machine learning algorithms to train them and give recommendations to the users. These algorithms need a large amount of data for a reasonable conversion rate. But for small applications, the available amount of data is

Recommendation systems provide recommendations based on user behavior andcontent data. User behavior and content data are fed to machine learning algorithms to train them and give recommendations to the users. These algorithms need a large amount of data for a reasonable conversion rate. But for small applications, the available amount of data is minimal, leading to high recommendation aberrations. Also, when an existing large scaled application with a high amount of available data uses a new recommendation system, it requires some time and testing to decide which recommendation algorithm is best suited to get higher conversion rates. This learning curve costs highly when the user base and data size are significantly high. In this thesis, A/B testing is used with manual intervention in the decision-making of recommendation systems. To understand the effectiveness of the recommendations, user interaction data is compared to compare experiences. Based on the comparisons, the experiments conclude the effectiveness of A/B testing for the recommendation system.
ContributorsVaidya, Yogesh Vinayak (Author) / Bansal, Ajay (Thesis advisor) / Findler, Michael (Committee member) / Chakravarthi, Bharatesh (Committee member) / Arizona State University (Publisher)
Created2023