Computer Engineering Commons^™

Development Of Enhanced Weed Detection System With Adaptive Thresholding, K-Means And Support Vector Machine, Dheeman Saha

Electronic Theses and Dissertations

This paper proposes a sophisticated classification process to segment the leaves of carrots from weeds (mostly Chamomile). In the early stages, of the plants’ development, both weeds and carrot leaves are intermixed with each other and have similar color texture. This makes it difficult to identify without the help of the domain experts. Therefore, it is essential to remove the weed regions so that the carrot plants can grow without any interruptions. The process of identifying the weeds become more challenging when both plant and weed regions overlap (inter-leaves). The proposed system addresses this problem by creating a sophisticated means …

Programming Of Collaborative Robot (Cobot) To Selectively Disassemble Products To Obtain Critical Materials, Brittany Felder

Research Opportunities for Engineering Undergraduates (ROEU) Program 2018-19

End-of-life products that contain critical materials, such as rare earth magnets, are often discarded even though there is remaining value that can be fed back into a remanufacturing process. This research aims to develop high throughput and economic value recovery from electric machines using collaborative robotics.

Scalable Clustering For Immune Repertoire Sequence Analysis, Prem Bhusal

Browse all Theses and Dissertations

The development of the next-generation sequencing technology has enabled systems immunology researchers to conduct detailed immune repertoire analysis at the molecule level. Large sequence datasets (e.g., millions of sequences) are being collected to comprehensively understand how the immune system of a patient evolves over different stages of disease development. A recent study has shown that the hierarchical clustering (HC) algorithm gives the best results for B-cell clones analysis - an important type of immune repertoire sequencing (IR-Seq) analysis. However, due to the inherent complexity, the classical hierarchical clustering algorithm does not scale well to large sequence datasets. Surprisingly, no algorithms …

Adaptive Microphone Array Systems With Neural Network Applications, Jazmine Marisol Covarrubias

Open Access Theses & Dissertations

A microphone array integrated with a neural network framework is proposed to enhance and optimize speech signals derived from environments prone to noise and room reflections that cause reverberation. Microphone arrays provide a way to capture spatial acoustic information for extracting voice input from ambient noise. In this study, we utilize and analyze established signal processing methods combined with different neural network architectures to achieve denoised and dereverberated speech signal results that are comparable with their clean, anechoic versions. The first stage of the proposed system involves using datasets containing anechoic speech recordings of speech utterances and convolving them with …

Artificial Intelligence In The Assessment Of Transmission And Distribution Systems Under Natural Disasters Using Machine Learning And Deep Learning Techniques In A Knowledge Discovery Framework, Rossana Villegas

Open Access Theses & Dissertations

Warming trends and increasing temperatures have been observed and reported by federal agencies, such as the National Oceanic and Atmospheric Administration (NOAA). Extreme-weather events, especially hurricanes, tornadoes and winter storms, are among the highly devastating natural disasters responsible for massive and prolonged power outages in Electrical Transmission and Distribution Systems (ETDS). Moreover, the failure rate probability of any system component under extreme-weather tends to increase in the impacted geographic area. This Dissertation proposes an Artificial Intelligence (AI) Decision Support System that can predict damage in the ETDS and allow operators to mitigate disastrous extreme weather events. The document reports the …

Context-Centric Affect Recognition From Paralinguistic Features Of Speech, Andreas Marpaung

Electronic Theses and Dissertations

As the field of affect recognition has progressed, many researchers have shifted from having unimodal approaches to multimodal ones. In particular, the trends in paralinguistic speech affect recognition domain have been to integrate other modalities such as facial expression, body posture, gait, and linguistic speech. Our work focuses on integrating contextual knowledge into paralinguistic speech affect recognition. We hypothesize that a framework to recognize affect through paralinguistic features of speech can improve its performance by integrating relevant contextual knowledge. This dissertation describes our research to integrate contextual knowledge into the paralinguistic affect recognition process from acoustic features of speech. We …

News - Screven-Jenkins Regional Library, Sharon Blank

Georgia Library Quarterly

No abstract provided.

Improvement Of Data-Intensive Applications Running On Cloud Computing Clusters, Ibrahim Adel Ibrahim

Electronic Theses and Dissertations

MapReduce, designed by Google, is widely used as the most popular distributed programming model in cloud environments. Hadoop, an open-source implementation of MapReduce, is a data management framework on large cluster of commodity machines to handle data-intensive applications. Many famous enterprises including Facebook, Twitter, and Adobe have been using Hadoop for their data-intensive processing needs. Task stragglers in MapReduce jobs dramatically impede job execution on massive datasets in cloud computing systems. This impedance is due to the uneven distribution of input data and computation load among cluster nodes, heterogeneous data nodes, data skew in reduce phase, resource contention situations, and …

Simulation, Analysis, And Optimization Of Heterogeneous Cpu-Gpu Systems, Christopher Giles

Electronic Theses and Dissertations

With the computing industry's recent adoption of the Heterogeneous System Architecture (HSA) standard, we have seen a rapid change in heterogeneous CPU-GPU processor designs. State-of-the-art heterogeneous CPU-GPU processors tightly integrate multicore CPUs and multi-compute unit GPUs together on a single die. This brings the MIMD processing capabilities of the CPU and the SIMD processing capabilities of the GPU together into a single cohesive package with new HSA features comprising better programmability, coherency between the CPU and GPU, shared Last Level Cache (LLC), and shared virtual memory address spaces. These advancements can potentially bring marked gains in heterogeneous processor performance and …

Rethinking Routing And Peering In The Era Of Vertical Integration Of Network Functions, Prasun Kanti Dey

Electronic Theses and Dissertations

Content providers typically control the digital content consumption services and are getting the most revenue by implementing an "all-you-can-eat" model via subscription or hyper-targeted advertisements. Revamping the existing Internet architecture and design, a vertical integration where a content provider and access ISP will act as unibody in a sugarcane form seems to be the recent trend. As this vertical integration trend is emerging in the ISP market, it is questionable if existing routing architecture will suffice in terms of sustainable economics, peering, and scalability. It is expected that the current routing will need careful modifications and smart innovations to ensure …

Scalable Map Information Dissemination For Connected And Automated Vehicle Systems, S. M. Osman Gani

Electronic Theses and Dissertations

Situational awareness in connected and automated vehicle (CAV) systems becomes particularly challenging in the presence of non-line of sight objects and/or objects beyond the sensing range of local onboard sensors. Despite the fact that fully autonomous driving requires the use of multiple redundant sensor systems, primarily including camera, radar, and LiDAR, the non-line of sight object detection problem still persists due to the inherent limitations of those sensing techniques. To tackle this challenge, the inter-vehicle communication system is envisioned that allows vehicles to exchange self-status updates aiming to extend their effective field of view and thus compensate for the limitations …

Machine Learning And Neural Networks For Real-Time Scheduling, Daniel Hureira, Christian Vartanian

Recent Advances in Real-Time Systems

This paper aims to serve as an efficient survey of the processes, problems, and methodologies surrounding the use of Neural Networks, specifically Hopfield-Type, in order to solve Hard-Real-Time Scheduling problems. Our primary goal is to demystify the field of Neural Networks research and properly describe the methods in which Real-Time scheduling problems may be approached when using neural networks. Furthermore, to give an introduction of sorts on this niche topic in a niche field. This survey is derived from four main papers, namely: “A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility” and “Scheduling Multiprocessor Job with Resource …

Secure Networked Control Via Software Rejuvenation, Paul Griffioen, Raffaele Romagnoli, Bruce H. Krogh, Bruno Sinopoli

Faculty Work Comprehensive List

Software rejuvenation has been proposed as a prevention mechanism against unanticipated and undetectable attacks on cyber-physical systems. Without needing to implement any detection algorithm, the system is periodically refreshed with a secure and trusted copy of the control software to eliminate any malicious modifications to the run-time code and data that may have corrupted the controller. Previous work has considered using software rejuvenation while being able to disconnect from the network when recovering from dangerous situations. In contrast, we consider using software rejuvenation in cases where a network connection is needed in order for proper recovery to occur. We present …

Selective Word Encoding For Effective Text Representation, Savaş Özkan, Akin Özkan

Turkish Journal of Electrical Engineering and Computer Sciences

Determining the category of a text document from its semantic content is highly motivated in the literature and it has been extensively studied in various applications. Also, the compact representation of the text is a fundamental step in achieving precise results for the applications and the studies are generously concentrated to improve its performance. In particular, the studies which exploit the aggregation of word-level representations are the mainstream techniques used in the problem. In this paper, we tackle text representation to achieve high performance in different text classification tasks. Throughout the paper, three critical contributions are presented. First, to encode …

No-Reference Image Denoising Quality Assessment, Si Lu

Computer Science Faculty Publications and Presentations

A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a noreference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individual existing quality metrics and …

Metadata-Based Image Collecting And Databasing For Sharing And Analysis, Xi Wu

Theses and Dissertations--Computer Science

Data collecting and preparing is generally considered a crucial process in data science projects. Especially for image data, adding semantic attributes when preparing image data provides much more insights for data scientists. In this project, we aim to implement a general-purpose central image data repository that allows image researchers to collect data with semantic properties as well as data query. One of our researchers has come up with the specific challenge of collecting images with weight data of infants in least developed countries with limited internet access. The rationale is to predict infant weights based on image data by applying …

A Diverse Band-Aware Dynamic Spectrum Access Architecture For Connectivity In Rural Communities, Vijay K. Shah

Theses and Dissertations--Computer Science

Ubiquitous connectivity plays an important role in improving the quality of life in terms of economic development, health and well being, social justice and equity, as well as in providing new educational opportunities. However, rural communities which account for 46% of the world's population lacks access to proper connectivity to avail such societal benefits, creating a huge "digital divide" between the urban and rural areas. A primary reason is that the Information and Communication Technologies (ICT) providers have less incentives to invest in rural areas due to lack of promising revenue returns. Existing research and industrial attempts in providing connectivity …

Novel Applications Of Machine Learning In Bioinformatics, Yi Zhang

Theses and Dissertations--Computer Science

Technological advances in next-generation sequencing and biomedical imaging have led to a rapid increase in biomedical data dimension and acquisition rate, which is challenging the conventional data analysis strategies. Modern machine learning techniques promise to leverage large data sets for finding hidden patterns within them, and for making accurate predictions. This dissertation aims to design novel machine learning-based models to transform biomedical big data into valuable biological insights. The research presented in this dissertation focuses on three bioinformatics domains: splice junction classification, gene regulatory network reconstruction, and lesion detection in mammograms.

A critical step in defining gene structures and mRNA …

Automated Network Security With Exceptions Using Sdn, Sergio A. Rivera Polanco

Theses and Dissertations--Computer Science

Campus networks have recently experienced a proliferation of devices ranging from personal use devices (e.g. smartphones, laptops, tablets), to special-purpose network equipment (e.g. firewalls, network address translation boxes, network caches, load balancers, virtual private network servers, and authentication servers), as well as special-purpose systems (badge readers, IP phones, cameras, location trackers, etc.). To establish directives and regulations regarding the ways in which these heterogeneous systems are allowed to interact with each other and the network infrastructure, organizations typically appoint policy writing committees (PWCs) to create acceptable use policy (AUP) documents describing the rules and behavioral guidelines that all campus network …

Depth Enhancement And Surface Reconstruction With Rgb/D Sequence, Xinxin Zuo

Theses and Dissertations--Computer Science

Surface reconstruction and 3D modeling is a challenging task, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. It is fundamental to many applications such as robot navigation, animation and scene understanding, industrial control and medical diagnosis. In this dissertation, I take advantage of the consumer depth sensors for surface reconstruction. Considering its limited performance on capturing detailed surface geometry, a depth enhancement approach is proposed in the first place to recovery small and rich geometric details with captured depth and color sequence. In addition to enhancing its spatial resolution, I present a …

Shaping The Future Of Trusted Digital Identity: The Idef Registry With Health Information Use Cases, Noreen Y. Whysel

Publications and Research

No abstract provided.

Distance,Time And Terms In First Story Detection, Fei Wang

Doctoral

First Story Detection (FSD) is an important application of online novelty detection within Natural Language Processing (NLP). Given a stream of documents, or stories, about news events in a chronological order, the goal of FSD is to identify the very first story for each event. While a variety of NLP techniques have been applied to the task, FSD remains challenging because it is still not clear what is the most crucial factor in defining the “story novelty”. Giventhesechallenges,thethesisaddressedinthisdissertationisthat the notion of novelty in FSD is multi-dimensional. To address this, the work presented has adopted a three dimensional analysis of the …

Music Information Retrieval For Irish Traditional Music Automatic Analysis Of Harmonic, Rhythmic, And Melodic Features For Efficient Key-Invariant Tune Recognition, Pierre Beauguitte

Doctoral

Music making and listening practices increasingly rely on techno logy,and,asaconsequence,techniquesdevelopedinmusicinformation retrieval (MIR) research are more readily available to end users, in par ticular via online tools and smartphone apps. However, the majority of MIRresearchfocusesonWesternpopandclassicalmusic,andthusdoes not address specificities of other musical idioms. Irishtraditionalmusic(ITM)ispopularacrosstheglobe,withregular sessionsorganisedonallcontinents. ITMisadistinctivemusicalidiom, particularly in terms of heterophony and modality, and these character istics can constitute challenges for existing MIR algorithms. The bene fitsofdevelopingMIRmethodsspecificallytailoredtoITMisevidenced by Tunepal, a query-by-playing tool that has become popular among ITM practitioners since its release in 2009. As of today, Tunepal is the state of the art for tune recognition in ITM. The research in …

Enhancing Synchrony In Multiplex Network Due To Rewiring Frequency, Sarbendu Rakshit, Bidesh K. Bera, Jürgen Kurths, Dibakar Ghosh

Journal Articles

Most of the previous studies on synchrony in multiplex networks have been investigated using different types of intralayer network architectures which are either static or temporal. Effect of a temporal layer on intralayer synchrony in a multilayered network still remains elusive. In this paper, we discuss intralayer synchrony in a multiplex network consisting of static and temporal layers and how a temporal layer influences other static layers to enhance synchrony simultaneously. We analytically derive local stability conditions for intralayer synchrony based on the master stability function approach. The analytically derived results are illustrated by numerical simulations on up to five-layers …

Cache Pressure-Aware Caching Scheme For Content-Centric Networking, Xi Luo, Ying An

Turkish Journal of Electrical Engineering and Computer Sciences

Content centric networking (CCN) is a new networking paradigm to meet the growing demand for content access in the future. Because of its important role in accelerating content retrieval and reducing network transmission load, in-network caching has become one of the core technologies in CCN and has attracted wide attention. The existing caching schemes often lack sufficient consideration of node cache status and the temporal validity of user requests, and thus the cache efficiency of the network is greatly reduced. In this paper, a cache pressure-aware caching scheme is proposed, which comprehensively takes into account various factors such as content …

Word Sense Disambiguation Using Semantic Kernels With Class-Based Term Values, Ayşe Berna Altinel, Murat Can Gani̇z, Bi̇lge Şi̇pal, Eren Can Erkaya, Onur Can Yücedağ, Muhammed Ali̇ Doğan

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, we propose several semantic kernels for word sense disambiguation (WSD). Our approaches adapt the intuition that class-based term values help in resolving ambiguity of polysemous words in WSD. We evaluate our proposed approaches with experiments, utilizing various sizes of training sets of disambiguated corpora (SensEval). With these experiments we try to answer the following questions: 1.) Do our semantic kernel formulations yield higher classification performance than traditional linear kernel?, 2.) Under which conditions a kernel design performs better than others?, 3.) Does the addition of class labels into standard term-document matrix improve the classification accuracy?, 4.) Is …

Identifying Key Topics Bearing Negative Sentiment On Twitter: Insights Concerning The 2015-2016 Zika Epidemic, Ravali Mamidi, Michele Miller, Tanvi Banerjee, William Romine, Amit Sheth

Publications

Background To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies’ efforts to disseminate relevant information to the public about Zika-related issues.

Objective The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed.

Methods Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised …

An Automated Negotiation System For Ecommerce Store Owners To Enable Flexible Product Pricing, Jake O'Halloran

Dissertations

If a store owner wishes to sell a product online, they traditionally have two options for deciding on a price. They can sell the product at a fixesd price like the products sold on sites like Amazon, or they can put the product in an auction and let demand from customers drive the final sales price like the products sold on sites like eBay. Both options have their pros and cons. An alternative option for deciding on a final sales price for the product is to enable negotiation on the product. With this, there is a dynamic nature to the …

Augmenting American Fuzzy Lop To Increase The Speed Of Bug Detection, Raviraj Mahajan

Dissertations

Whitebox fuzz testing is a vital part of the software testing process in the software development life cycle (SDLC). It is used for bug detection and security vulnerability checking as well. But current tools lack the ability to detect all the bugs and cover the entire code under test in a reasonable time. This study will explore some of the various whitebox fuzzing techniques and tools (AFL, SAGE, Driller, etc.) currently in use followed by a discussion of their strategies and the challenges facing them. One of the most popular state-of-the-art fuzzers, American Fuzzy Lop (AFL) will be discussed in …

Investigation Into The Perceptually Informed Data For Environmental Sound Recognition, Chenglin Kang

Dissertations

Environmental sound is rich source of information that can be used to infer contexts. With the rise in ubiquitous computing, the desire of environmental sound recognition is rapidly growing. Primarily, the research aims to recognize the environmental sound using the perceptually informed data. The initial study is concentrated on understanding the current state-of-the-art techniques in environmental sound recognition. Then those researches are evaluated by a critical review of the literature. This study extracts three sets of features: Mel Frequency Cepstral Coefficients, Mel-spectrogram and sound texture statistics. Two kinds machine learning algorithms are cooperated with appropriate sound features. The models are …