Computer Engineering Commons^™

Audio To Architecture: House Music As A Form Generator, Polina Timchenko

Architecture Undergraduate Honors Theses

Contemporary music undergoes similar process of creation to that of the design process through computation and variation. House music as a representation of contemporary culture has a layered structure that allows specific characteristics to identify it as house music. Song components can vary and mix in different orders that form new dynamic compositions. I am going to explore the idea that every house music component can be translated into geometry with the use of parametric design techniques.

Distance-Based Cluster Head Election For Mobile Sensing, Ruairí De Fréin, Liam O'Farrell

Conference papers

Energy-efficient, fair, stochastic leader-selection algorithms are designed for mobile sensing scenarios which adapt the sensing strategy depending on the mobile sensing topology. Methods for electing a cluster head are crucially important when optimizing the trade-off between the number of peer-to- peer interactions between mobiles and client-server interactions with a cloud-hosted application server. The battery-life of mobile devices is a crucial constraint facing application developers who are looking to use the convergence of mobile computing and cloud computing to perform environmental sensing. We exploit the mobile network topology, specifically the location of mobiles with respect to the gateway device, to stochastically …

Localization Using Convolutional Neural Networks, Shannon D. Fong

Computer Engineering

With the increased accessibility to powerful GPUs, ability to develop machine learning algorithms has increased significantly. Coupled with open source deep learning frameworks, average users are now able to experiment with convolutional neural networks (CNNs) to solve novel problems. This project sought to train a CNN capable of classifying between various locations within a building. A single continuous video was taken while standing at each desired location so that every class in the neural network was represented by a single video. Each location was given a number to be used for classification and the video was subsequently titled locX. These …

A Scalable, Chunk-Based Slicer For Cooperative 3d Printing, Jace J. Mcpherson

Computer Science and Computer Engineering Undergraduate Honors Theses

Cooperative 3D printing is an emerging technology that aims to increase the 3D printing speed and to overcome the size limit of the printable object by having multiple mobile 3D printers (printhead-carrying mobile robots) work together on a single print job on a factory floor. It differs from traditional layer-by-layer 3D printing due to requiring multiple mobile printers to work simultaneously without interfering with each other. Therefore, a new approach for slicing a digital model and generating commands for the mobile printers is needed, which has not been discussed in literature before. We propose a chunk-by-chunk based slicer that divides …

Assessing Risk In Iot Devices, David Levitsky

Master's Theses

The explosive growth of the Internet of Things ecosystem has thrust these devices into the center of our lives. Unfortunately, the desire to create these devices has been stronger than the one to secure them. Recent attacks have shown us ignoring security in Internet of Things devices can cause severe harm in both a digital and physical sense. This thesis outlines a framework for developers and managers to assess the risk of IoT devices using a weighted scoring system across five different categories. Our case studies suggest that devices with higher security considerations have a better security posture and lower …

Evaluating Load Adjusted Learning Strategies For Client Service Levels Prediction From Cloud-Hosted Video Servers, Ruairí De Fréin, Obinna Izima, Mark Davis

Conference papers

Network managers that succeed in improving the accuracy of client video service level predictions, where the video is deployed in a cloud infrastructure, will have the ability to deliver responsive, SLA-compliant service to their customers. Meeting up-time guarantees, achieving rapid first-call resolution, and minimizing time-to-recovery af- ter video service outages will maintain customer loyalty.

To date, regression-based models have been applied to generate these predictions for client machines using the kernel metrics of a server clus- ter. The effect of time-varying loads on cloud-hosted video servers, which arise due to dynamic user requests have not been leveraged to improve prediction …

Scale-Out Algorithm For Apache Storm In Saas Environment, Ravi Kiran Puttaswamy

School of Computing: Dissertations, Theses, and Student Research

The main appeal of the Cloud is in its cost effective and flexible access to computing power. Apache Storm is a data processing framework used to process streaming data. In our work we explore the possibility of offering Apache Storm as a software service. Further, we take advantage of the cgroups feature in Storm to divide the computing power of worker machine into smaller units to be offered to users. We predict that the compute bounds placed on the cgroups could be used to approximate the state of the workflow. We discuss the limitations of the current schedulers in facilitating …

Reducing The Tail Latency Of A Distributed Nosql Database, Jun Wu

School of Computing: Dissertations, Theses, and Student Research

The request latency is an important performance metric of a distributed database, such as the popular Apache Cassandra, because of its direct impact on the user experience. Specifically, the latency of a read or write request is defined as the total time interval from the instant when a user makes the request to the instant when the user receives the request, and it involves not only the actual read or write time at a specific database node, but also various types of latency introduced by the distributed mechanism of the database. Most of the current work focuses only on reducing …

Study On The Pattern Recognition Enhancement For Matrix Factorizations With Automatic Relevance Determination, Hau Tao

Electronic Theses, Projects, and Dissertations

Learning the parts of objects have drawn more attentions in computer science recently, and they have been playing the important role in computer applications such as object recognition, self-driving cars, and image processing, etc… However, the existing research such as traditional non-negative matrix factorization (NMF), principal component analysis (PCA), and vector quantitation (VQ) has not been discovering the ground-truth bases which are basic components representing objects. On this thesis, I am proposed to study on pattern recognition enhancement combined non-negative matrix factorization (NMF) with automatic relevance determination (ARD). The main point of this research is to propose a new technique …

Organize Events Mobile Application, Thakshak Mani Chandra Reddy Gudimetla

Electronic Theses, Projects, and Dissertations

In a big organization there are many events organized every day. To know about the events, we typically need to check an events page, rely on flyers or on distributed pamphlets or through word of mouth. To register for an event a user now a days typically does this online which involves inputting user details. At the event, the user either signs a sheet of paper or enters credentials in a web page loaded on a tablet or other electronic device. Typically, this is a time-consuming process with many redundancies like entering user details every time the user wants to …

Social Network For Software Developers, Sanket Prabhakar Jadhav

Electronic Theses, Projects, and Dissertations

This project is the design and implementation of a web-based message board for software developers. The purpose of “Social Network for Software Developers” is to connect inexperienced software developers with experienced software developers.

California State University, San Bernardino Chatbot, Krutarth Desai

Electronic Theses, Projects, and Dissertations

Now-a-days the chatbot development has been moving from the field of Artificial-Intelligence labs to the desktops and mobile domain experts. In the fastest growing technology world, most smartphone users spend major time in the messaging apps such as Facebook messenger. A chatbot is a computer program that uses messaging channels to interact with users using natural Languages. Chatbot uses appropriate mapping techniques to transform user inputs into a relational database and fetch the data by calling an existing API and then sends an appropriate response to the user to drive its chats. Drawbacks include the need to learn and use …

Amplifying The Prediction Of Team Performance Through Swarm Intelligence And Machine Learning, Erick Michael Harris

Master's Theses

Modern companies are increasingly relying on groups of individuals to reach organizational goals and objectives, however many organizations struggle to cultivate optimal teams that can maximize performance. Fortunately, existing research has established that group personality composition (GPC), across five dimensions of personality, is a promising indicator of team effectiveness. Additionally, recent advances in technology have enabled groups of humans to form real-time, closed-loop systems that are modeled after natural swarms, like flocks of birds and colonies of bees. These Artificial Swarm Intelligences (ASI) have been shown to amplify performance in a wide range of tasks, from forecasting financial markets to …

Collaborative Robotic Path Planning For Industrial Spraying Operations On Complex Geometries, Steven Brown

Graduate Theses and Dissertations

Implementation of automated robotic solutions for complex tasks currently faces a few major hurdles. For instance, lack of effective sensing and task variability – especially in high-mix/low-volume processes – creates too much uncertainty to reliably hard-code a robotic work cell. Current collaborative frameworks generally focus on integrating the sensing required for a physically collaborative implementation. While this paradigm has proven effective for mitigating uncertainty by mixing human cognitive function and fine motor skills with robotic strength and repeatability, there are many instances where physical interaction is impractical but human reasoning and task knowledge is still needed. The proposed framework consists …

Automation Of Post-Earthquake Civil Infrastructure Reconnaissance, Jack Bergquist

Architectural Engineering

Traditionally post-earthquake structural engineering reconnaissance consists of a team of experts who are deployed to the field to record and capture earthquake damage data, which is later uploaded into online repositories. Despite many advances to these data archives in recent years, the entries in online repositories often have limited metadata which make it difficult and time consuming to extract specific damage evidence that can be used for meaningful analysis. This report outlines the author’s contributions to overcoming these challenges via the development of a neural network that automatically filters and classifies post-earthquake civil infrastructure damage data after a seismic event. …

Sensor-Based Human Activity Recognition Using Bidirectional Lstm For Closely Related Activities, Arumugam Thendramil Pavai

Electronic Theses, Projects, and Dissertations

Recognizing human activities using deep learning methods has significance in many fields such as sports, motion tracking, surveillance, healthcare and robotics. Inertial sensors comprising of accelerometers and gyroscopes are commonly used for sensor based HAR. In this study, a Bidirectional Long Short-Term Memory (BLSTM) approach is explored for human activity recognition and classification for closely related activities on a body worn inertial sensor data that is provided by the UTD-MHAD dataset. The BLSTM model of this study could achieve an overall accuracy of 98.05% for 15 different activities and 90.87% for 27 different activities performed by 8 persons with 4 …

Crude Oil Prices Forecasting: Time Series Vs. Svr Models, Xin James He

Journal of International Technology and Information Management

This research explores the weekly crude oil price data from U.S. Energy Information Administration over the time period 2009 - 2017 to test the forecasting accuracy by comparing time series models such as simple exponential smoothing (SES), moving average (MA), and autoregressive integrated moving average (ARIMA) against machine learning support vector regression (SVR) models. The main purpose of this research is to determine which model provides the best forecasting results for crude oil prices in light of the importance of crude oil price forecasting and its implications to the economy. While SVR is often considered the best forecasting model in …

Table Of Contents Jitim Vol 27 Issue 3, 2018

Journal of International Technology and Information Management

Table of Contents

The Relationship Between Organizational Resources And Green It/S Adoption: A Rbv Approach, Lutfus Sayeed, Alberto Onetti

Journal of International Technology and Information Management

ABSTRACT

The objective of the present study was to empirically explore the impact of the implementation of Green IT/S measures on organizational resources in the US and European firms. The study examined the influence of reconfiguration of resources within a firm while adopting various Green IT/S practices and technologies. Green IT/S implementation requires resource commitment from organizations (Bose and Luo, 2011). What are these resources and how do they affect the extent of Green IT/S measures adopted by businesses? Resource Based View (RBV) of the firm was used as the theoretical framework of the study. The relationship between the adoption …

Feature-Based Transfer Learning In Natural Language Processing, Jianfei Yu

Dissertations and Theses Collection (Open Access)

In the past few decades, supervised machine learning approach is one of the most important methodologies in the Natural Language Processing (NLP) community. Although various kinds of supervised learning methods have been proposed to obtain the state-of-the-art performance across most NLP tasks, the bottleneck of them lies in the heavy reliance on the large amount of manually annotated data, which is not always available in our desired target domain/task. To alleviate the data sparsity issue in the target domain/task, an attractive solution is to find sufficient labeled data from a related source domain/task. However, for most NLP applications, due to …