Computer Engineering Commons^™

Breadcrumbs: Privacy As A Privilege, Prachi Bhardwaj

Capstones

Breadcrumbs: Privacy as a Privilege Abstract

By: Prachi Bhardwaj

In 2017, the world saw more data breaches than in any year prior. The count was more than the all-time high record in 2016, which was 40 percent more than the year before that.

That’s because consumer data is incredibly valuable today. In the last three decades, data storage has gone from being stored physically to being stored almost entirely digitally, which means consumer data is more accessible and applicable to business strategies. As a result, companies are gathering data in ways previously unknown to the average consumer, and hackers are …

Proactive Sequential Resource (Re)Distribution For Improving Efficiency In Urban Environments, Supriyo Ghosh

Dissertations and Theses Collection (Open Access)

Due to the increasing population and lack of coordination, there is a mismatch in supply and demand of common resources (e.g., shared bikes, ambulances, taxis) in urban environments, which has deteriorated a wide variety of quality of life metrics such as success rate in issuing shared bikes, response times for emergency needs, waiting times in queues etc. Thus, in my thesis, I propose efficient algorithms that optimise the quality of life metrics by proactively redistributing the resources using intelligent operational (day-to-day) and strategic (long-term) decisions in the context of urban transportation and health & safety. For urban transportation, Bike Sharing …

Automated Program Profiling And Analysis For Managing Heterogeneous Memory Systems, Adam Palmer Howard

Masters Theses

Many promising memory technologies, such as non-volatile, storage-class memories and high-bandwidth, on-chip RAMs, are beginning to emerge. Since each of these new technologies present tradeoffs distinct from conventional DRAMs, next-generation systems are likely to include multiple tiers of memory storage, each with their own type of devices. To efficiently utilize the available hardware, such systems will need to alter their data management strategies to consider the performance and capabilities provided by each tier.

This work explores a variety of cross-layer strategies for managing application data in heterogeneous memory systems. We propose different program profiling-based techniques to automatically partition program allocation …

A Framework For Clustering And Adaptive Topic Tracking On Evolving Text And Social Media Data Streams., Gopi Chand Nutakki

Electronic Theses and Dissertations

Recent advances and widespread usage of online web services and social media platforms, coupled with ubiquitous low cost devices, mobile technologies, and increasing capacity of lower cost storage, has led to a proliferation of Big data, ranging from, news, e-commerce clickstreams, and online business transactions to continuous event logs and social media expressions. These large amounts of online data, often referred to as data streams, because they get generated at extremely high throughputs or velocity, can make conventional and classical data analytics methodologies obsolete. For these reasons, the issues of management and analysis of data streams have been researched extensively …

Strong-Dism: A First Attempt To A Dynamically Typed Assembly Language (D-Tal), Ivory Hernandez

USF Tampa Graduate Theses and Dissertations

Dynamically Typed Assembly Language (D-TAL) is not only a lightweight and effective solution to the gap generated by the drop in security produced by the translation of high-level language instructions to low-level language instructions, but it considerably eases up the burden generated by the level of complexity required to implement typed assembly languages statically. Although there are tradeoffs between the static and dynamic approaches, focusing on a dynamic approach leads to simpler, easier to reason about, and more feasible ways to understand deployment of types over monomorphically-typed or untyped intermediate languages. On this occasion, DISM, a simple but powerful and …

Adaft: A Resource-Efficient Framework For Adaptive Fault-Tolerance In Cyber-Physical Systems, Ye Xu

Doctoral Dissertations

Cyber-physical systems frequently have to use massive redundancy to meet application requirements for high reliability. While such redundancy is required, it can be activated adaptively, based on the current state of the controlled plant. Most of the time the physical plant is in a state that allows for a lower level of fault-tolerance. Avoiding the continuous deployment of massive fault-tolerance will greatly reduce the workload of CPSs. In this dissertation, we demonstrate a software simulation framework (AdaFT) that can automatically generate the sub-spaces within which our adaptive fault-tolerance can be applied. We also show the theoretical benefits of AdaFT, and …

Improving Hpc Communication Library Performance On Modern Architectures, Matthew G. F. Dosanjh

Computer Science ETDs

As high-performance computing (HPC) systems advance towards exascale (10^18 operations per second), they must leverage increasing levels of parallelism to achieve their performance goals. In addition to increased parallelism, machines of that scale will have strict power limitations placed on them. One direction currently being explored to alleviate those issues are many-core processors such as Intel’s Xeon Phi line. Many-core processors sacrifice clock speed and core complexity, such as out of order pipelining, to increase the number of cores on a die. While this increases floating point throughput, it can reduce the performance of serialized, synchronized, and latency sensitive code …

Modeling Energy Consumption Of High-Performance Applications On Heterogeneous Computing Platforms, Gary D. Lawson Jr.

Computational Modeling & Simulation Engineering Theses & Dissertations

Achieving Exascale computing is one of the current leading challenges in High Performance Computing (HPC). Obtaining this next level of performance will allow more complex simulations to be run on larger datasets and offer researchers better tools for data processing and analysis. In the dawn of Big Data, the need for supercomputers will only increase. However, these systems are costly to maintain because power is expensive. Thus, a better understanding of power and energy consumption is required such that future hardware can benefit.

Available power models accurately capture the relationship to the number of cores and clock-rate, however the relationship …

Comparing And Improving Facial Recognition Method, Brandon Luis Sierra

Electronic Theses, Projects, and Dissertations

Facial recognition is the process in which a sample face can be correctly identified by a machine amongst a group of different faces. With the never-ending need for improvement in the fields of security, surveillance, and identification, facial recognition is becoming increasingly important. Considering this importance, it is imperative that the correct faces are recognized and the error rate is as minimal as possible. Despite the wide variety of current methods for facial recognition, there is no clear cut best method. This project reviews and examines three different methods for facial recognition: Eigenfaces, Fisherfaces, and Local Binary Patterns to determine …

The Future Is Coming : Research On Maritime Communication Technology For Realization Of Intelligent Ship And Its Impacts On Future Maritime Management, Jiacheng Ke

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Research On Improving Navigation Safety Based On Big Data And Cloud Computing Technology For Qiongzhou Strait, Rui Wang

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Operating System Identification By Ipv6 Communication Using Machine Learning Ensembles, Adrian Ordorica

Graduate Theses and Dissertations

Operating system (OS) identification tools, sometimes called fingerprinting tools, are essential for the reconnaissance phase of penetration testing. While OS identification is traditionally performed by passive or active tools that use fingerprint databases, very little work has focused on using machine learning techniques. Moreover, significantly more work has focused on IPv4 than IPv6. We introduce a collaborative neural network ensemble that uses a unique voting system and a random forest ensemble to deliver accurate predictions. This approach uses IPv6 features as well as packet metadata features for OS identification. Our experiment shows that our approach is valid and we achieve …

Improving Pattern Recognition And Neural Network Algorithms With Applications To Solar Panel Energy Optimization, Ernesto Zamora Ramos

UNLV Theses, Dissertations, Professional Papers, and Capstones

Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize …

A Data Science Pipeline For Educational Data : A Case Study Using Learning Catalytics In The Active Learning Classroom., Asuman Cagla Acun Sener

Electronic Theses and Dissertations

This thesis presents an applied data science methodology on a set of University of Louisville, Speed School of Engineering student data. We used data mining and classic statistical techniques to help educational researchers quickly see the data trends and peculiarities. Our data includes scores and information about two Engineering Fundamental Class. The format of these classes is called an inverted classroom model or flipped class. The purpose of this study is to analyze the data in order to uncover potentially hidden information, tell interesting stories about the data, examine student learning behavior and learning performance in an active learning environment, …

Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi

Electronic Theses and Dissertations

While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race …

Distributed Knowledge Discovery For Diverse Data, Hossein Hamooni

Computer Science ETDs

In the era of new technologies, computer scientists deal with massive data of size hundreds of terabytes. Smart cities, social networks, health care systems, large sensor networks, etc. are constantly generating new data. It is non-trivial to extract knowledge from big datasets because traditional data mining algorithms run impractically on such big datasets. However, distributed systems have come to aid this problem while introducing new challenges in designing scalable algorithms. The transition from traditional algorithms to the ones that can be run on a distributed platform should be done carefully. Researchers should design the modern distributed algorithms based on the …

Belief-Space Planning For Resourceful Manipulation And Mobility, Dirk Ruiken

Doctoral Dissertations

Robots are increasingly expected to work in partially observable and unstructured environments. They need to select actions that exploit perceptual and motor resourcefulness to manage uncertainty based on the demands of the task and environment. The research in this dissertation makes two primary contributions. First, it develops a new concept in resourceful robot platforms called the UMass uBot and introduces the sixth and seventh in the uBot series. uBot-6 introduces multiple postural configurations that enable different modes of mobility and manipulation to meet the needs of a wide variety of tasks and environmental constraints. uBot-7 extends this with the use …

How Technology Is Reshaping Financial Services: Essays On Consumer Behavior In Card, Channel And Cryptocurrency Services, Dan Geng

Dissertations and Theses Collection

The financial services sector has seen dramatic technological innovations in the last several years associated with the “fintech revolution.” Major changes have taken place in channel management, credit card rewards marketing, cryptocurren-cy, and wealth management, and have influenced consumers’ banking behavior in different ways. As a consequence, there has been a growing demand for banks to rethink their business models and operations to adapt to changing consumer be-havior and counter the competitive pressure from other banks and non-bank play-ers. In this dissertation, I study consumer behavior related to different aspects of financial innovation by addressing research questions that are motivated …

Speech Based Machine Learning Models For Emotional State Recognition And Ptsd Detection, Debrup Banerjee

Electrical & Computer Engineering Theses & Dissertations

Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a …

Underwater Computer Vision - Fish Recognition, Spencer Chang, Austin Otto

Computer Engineering

The Underwater Computer Vision – Fish Recognition project includes the design and implementation of a device that can withstand staying underwater for a duration of time, take pictures of underwater creatures, such as fish, and be able to identify certain fish. The system is meant to be cheap to create, yet still able to process the images it takes and identify the objects in the pictures with some accuracy. The device can output its results to another device or an end user.

Recommending Personalized Schedules In Urban Environments, Cen Chen

Dissertations and Theses Collection

In this thesis, we are broadly interested in solving real world problems that involve decision support for coordinating agent movements in dynamic urban environments, where people are agents exhibiting different human behavior patterns and preferences. The rapid development of mobile technologies makes it easier to capture agent behavioral and preference information. Such rich agent specific information, coupled with the explosive growth of computational power, opens many opportunities that we could potentially leverage, to better guide/influence the agents in urban environments. The purpose of this thesis is to investigate how we can effectively and efficiently guide and coordinate the agents with …

Adaptive Region-Based Approaches For Cellular Segmentation Of Bright-Field Microscopy Images, Hady Ahmady Phoulady

USF Tampa Graduate Theses and Dissertations

Microscopy image processing is an emerging and quickly growing field in medical imaging research area. Recent advancements in technology including higher computation power, larger and cheaper storage modules, and more efficient and faster data acquisition devices such as whole-slide imaging scanners contributed to the recent microscopy image processing research advancement. Most of the methods in this research area either focus on automatically process images and make it easier for pathologists to direct their focus on the important regions in the image, or they aim to automate the whole job of experts including processing and classifying images or tissues that leads …

Target Detection With Neural Network Hardware, Hollis Bui, Garrett Massman, Nikolas Spangler, Jalen Tarvin, Luke Bechtel, Kevin Dunn, Shawn Bradford

Chancellor’s Honors Program Projects

No abstract provided.

Hexarray: A Novel Self-Reconfigurable Hardware System, Fady Hussein

Boise State University Theses and Dissertations

Evolvable hardware (EHW) is a powerful autonomous system for adapting and finding solutions within a changing environment. EHW consists of two main components: a reconfigurable hardware core and an evolutionary algorithm. The majority of prior research focuses on improving either the reconfigurable hardware or the evolutionary algorithm in place, but not both. Thus, current implementations suffer from being application oriented and having slow reconfiguration times, low efficiencies, and less routing flexibility. In this work, a novel evolvable hardware platform is proposed that combines a novel reconfigurable hardware core and a novel evolutionary algorithm.

The proposed reconfigurable hardware core is a …

A Hybrid Partially Reconfigurable Overlay Supporting Just-In-Time Assembly Of Custom Accelerators On Fpgas, Zeyad Tariq Aklah

Graduate Theses and Dissertations

The state of the art in design and development flows for FPGAs are not sufficiently mature to allow programmers to implement their applications through traditional software development flows. The stipulation of synthesis as well as the requirement of background knowledge on the FPGAs' low-level physical hardware structure are major challenges that prevent programmers from using FPGAs. The reconfigurable computing community is seeking solutions to raise the level of design abstraction at which programmers must operate, and move the synthesis process out of the programmers' path through the use of overlays. A recent approach, Just-In-Time Assembly (JITA), was proposed that enables …

Peeking Into The Other Half Of The Glass : Handling Polarization In Recommender Systems., Mahsa Badami

Electronic Theses and Dissertations

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users' interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate …

Using A Multi Variate Pattern Analysis (Mvpa) Approach To Decode Fmri Responses To Fear And Anxiety., Sajjad Torabian Esfahani

Electronic Theses and Dissertations

This study analyzed fMRI responses to fear and anxiety using a Multi Variate Pattern Analysis (MVPA) approach. Compared to conventional univariate methods which only represent regions of activation, MVPA provides us with more detailed patterns of voxels. We successfully found different patterns for fear and anxiety through separate classification attempts in each subject’s representational space. Further, we transformed all the individual models into a standard space to do group analysis. Results showed that subjects share a more common fear response. Also, the amygdala and hippocampus areas are more important for differentiating fear than anxiety.

Music Feature Matching Using Computer Vision Algorithms, Mason Hollis

Computer Science and Computer Engineering Undergraduate Honors Theses

This paper seeks to establish the validity and potential benefits of using existing computer vision techniques on audio samples rather than traditional images in order to consistently and accurately identify a song of origin from a short audio clip of potentially noisy sound. To do this, the audio sample is first converted to a spectrogram image, which is used to generate SURF features. These features are compared against a database of features, which have been previously generated in a similar fashion, in order to find the best match. This algorithm has been implemented in a system that can run as …

Machs: Mitigating The Achilles Heel Of The Cloud Through High Availability And Performance-Aware Solutions, Manar Jammal

Electronic Thesis and Dissertation Repository

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a component’s HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their …

Network-On-Chip Based H.264 Video Decoder On A Field Programmable Gate Array, Ian Barge

Master's Theses (2009 -)

This thesis develops the first fully network-on-chip (NoC) based h.264 video decoder implemented in real hardware on a field programmable gate array (FPGA). This thesis starts with an overview of the h.264 video coding standard and an introduction to the NoC communication paradigm. Following this, a series of processing elements (PEs) are developed which implement the component algorithms making up the h.264 video decoder. These PEs, described primarily in VHDL with some Verilog and C, are then mapped to an NoC which is generated using the CONNECT NoC generation tool. To demonstrate the scalability of the proposed NoC based design, …