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Full-Text Articles in Computer Engineering

Sparsity In Machine Learning: An Information Selecting Perspective, Siwei Feng Jan 2019

Sparsity In Machine Learning: An Information Selecting Perspective, Siwei Feng

Doctoral Dissertations

Today we are living in a world awash with data. Large volumes of data are acquired, analyzed and applied to tasks through machine learning algorithms in nearly every area of science, business, and industry. For example, medical scientists analyze the gene expression data from a single specimen to learn the underlying causes of disease (e.g. cancer) and choose the best treatment; retailers can know more about customers' shopping habits from retail data to adjust their business strategies to better appeal to customers; suppliers can enhance supply chain success through supply chain systems built on knowledge sharing. However, it is ...


Power Laws In Complex Graphs: Parsimonious Generative Models, Similarity Testing Algorithms, And The Origins, Shan Lu Jan 2018

Power Laws In Complex Graphs: Parsimonious Generative Models, Similarity Testing Algorithms, And The Origins, Shan Lu

Doctoral Dissertations

This dissertation mainly discussed topics related to power law graphs, including graph similarity testing algorithms and power law generative models.

For graph similarity testing, we proposed a method based on the mathematical theory of diffusion over manifolds using random walks over graphs. We show that our method not only distinguishes between graphs with different degree distributions, but also graphs with the same degree distributions. We compare the undirected power law graphs generated by Barabasi-Albert model and directed power law graphs generated by Krapivsky's model to the random graphs generated by Erdos-Renyi model. We also compare power law graphs generated ...


Transiency-Driven Resource Management For Cloud Computing Platforms, Prateek Sharma Jan 2018

Transiency-Driven Resource Management For Cloud Computing Platforms, Prateek Sharma

Doctoral Dissertations

Modern distributed server applications are hosted on enterprise or cloud data centers that provide computing, storage, and networking capabilities to these applications. These applications are built using the implicit assumption that the underlying servers will be stable and normally available, barring for occasional faults. In many emerging scenarios, however, data centers and clouds only provide transient, rather than continuous, availability of their servers. Transiency in modern distributed systems arises in many contexts, such as green data centers powered using renewable intermittent sources, and cloud platforms that provide lower-cost transient servers which can be unilaterally revoked by the cloud operator.

Transient ...


Multi-Sensor Localization And Tracking In Disaster Management And Indoor Wayfinding For Visually Impaired Users, Zhuorui Yang Jan 2018

Multi-Sensor Localization And Tracking In Disaster Management And Indoor Wayfinding For Visually Impaired Users, Zhuorui Yang

Doctoral Dissertations

This dissertation proposes a series of multi-sensor localization and tracking algorithms particularly developed for two important application domains, which are disaster management and indoor wayfinding for blind and visually impaired (BVI) users. For disaster management, we developed two different localization algorithms, one each for Radio Frequency Identification (RFID) and Bluetooth Low Energy (BLE) technology, which enable the disaster management system to track patients in real-time. Both algorithms work in the absence of any pre-deployed infrastructure along with smartphones and wearable devices. Regarding indoor wayfinding for BVI users, we have explored several types of indoor positioning techniques including BLE-based, inertial, visual ...


Computational Imaging Approach To Recovery Of Target Coordinates Using Orbital Sensor Data, Michael D. Vaughan Aug 2017

Computational Imaging Approach To Recovery Of Target Coordinates Using Orbital Sensor Data, Michael D. Vaughan

Doctoral Dissertations

This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data.

Using physical targets and sensors in this scenario would be ...


Modeling The Consumer Acceptance Of Retail Service Robots, So Young Song Aug 2017

Modeling The Consumer Acceptance Of Retail Service Robots, So Young Song

Doctoral Dissertations

This study uses the Computers Are Social Actors (CASA) and domestication theories as the underlying framework of an acceptance model of retail service robots (RSRs). The model illustrates the relationships among facilitators, attitudes toward Human-Robot Interaction (HRI), anxiety toward robots, anticipated service quality, and the acceptance of RSRs. Specifically, the researcher investigates the extent to which the facilitators of usefulness, social capability, the appearance of RSRs, and the attitudes toward HRI affect acceptance and increase the anticipation of service quality. The researcher also tests the inhibiting role of pre-existing anxiety toward robots on the relationship between these facilitators and attitudes ...


Wide-Area Measurement-Driven Approaches For Power System Modeling And Analytics, Hesen Liu Aug 2017

Wide-Area Measurement-Driven Approaches For Power System Modeling And Analytics, Hesen Liu

Doctoral Dissertations

This dissertation presents wide-area measurement-driven approaches for power system modeling and analytics. Accurate power system dynamic models are the very basis of power system analysis, control, and operation. Meanwhile, phasor measurement data provide first-hand knowledge of power system dynamic behaviors. The idea of building out innovative applications with synchrophasor data is promising.

Taking advantage of the real-time wide-area measurements, one of phasor measurements’ novel applications is to develop a synchrophasor-based auto-regressive with exogenous inputs (ARX) model that can be updated online to estimate or predict system dynamic responses.

Furthermore, since auto-regressive models are in a big family, the ARX model ...


A Probabilistic Software Framework For Scalable Data Storage And Integrity Check, Sisi Xiong Aug 2017

A Probabilistic Software Framework For Scalable Data Storage And Integrity Check, Sisi Xiong

Doctoral Dissertations

Data has overwhelmed the digital world in terms of volume, variety and velocity. Data- intensive applications are facing unprecedented challenges. On the other hand, computation resources, such as memory, suffer from shortage comparing to data scale. However, in certain applications, it is a must to process large amount of data in a time efficient manner. Probabilistic approaches are compromises between these three perspectives: large amount of data, limited computation resources and high time efficiency, in the sense that those approaches cannot guarantee 100% correctness, their error rates, however, are predictable and adjustable depending on available computation resources and time constraints ...


Image Processing And Understanding Based On Graph Similarity Testing: Algorithm Design And Software Development, Jieqi Kang Jan 2017

Image Processing And Understanding Based On Graph Similarity Testing: Algorithm Design And Software Development, Jieqi Kang

Doctoral Dissertations

Image processing and understanding is a key task in the human visual system. Among all related topics, content based image retrieval and classification is the most typical and important problem. Successful image retrieval/classification models require an effective fundamental step of image representation and feature extraction. While traditional methods are not capable of capturing all structural information on the image, using graph to represent the image is not only biologically plausible but also has certain advantages.

Graphs have been widely used in image related applications. Traditional graph-based image analysis models include pixel-based graph-cut techniques for image segmentation, low-level and high-level ...


Hyperspectral Data Acquisition And Its Application For Face Recognition, Woon Cho Dec 2015

Hyperspectral Data Acquisition And Its Application For Face Recognition, Woon Cho

Doctoral Dissertations

Current face recognition systems are rife with serious challenges in uncontrolled conditions: e.g., unrestrained lighting, pose variations, accessories, etc. Hyperspectral imaging (HI) is typically employed to counter many of those challenges, by incorporating the spectral information within different bands. Although numerous methods based on hyperspectral imaging have been developed for face recognition with promising results, three fundamental challenges remain: 1) low signal to noise ratios and low intensity values in the bands of the hyperspectral image specifically near blue bands; 2) high dimensionality of hyperspectral data; and 3) inter-band misalignment (IBM) correlated with subject motion during data acquisition.

This ...


Compressed Sensing In Resource-Constrained Environments: From Sensing Mechanism Design To Recovery Algorithms, Shuangjiang Li Aug 2015

Compressed Sensing In Resource-Constrained Environments: From Sensing Mechanism Design To Recovery Algorithms, Shuangjiang Li

Doctoral Dissertations

Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the smartphone platform, or a band-limited environment like visual sensor network (VSNs). There are several challenges to perform sensing due to the characteristic of these platforms, including, for example, needing active user involvement, computational and storage limitations and lower transmission capabilities. This dissertation focuses on the study ...


Physically Equivalent Intelligent Systems For Reasoning Under Uncertainty At Nanoscale, Santosh Khasanvis Jan 2015

Physically Equivalent Intelligent Systems For Reasoning Under Uncertainty At Nanoscale, Santosh Khasanvis

Doctoral Dissertations

Machines today lack the inherent ability to reason and make decisions, or operate in the presence of uncertainty. Machine-learning methods such as Bayesian Networks (BNs) are widely acknowledged for their ability to uncover relationships and generate causal models for complex interactions. However, their massive computational requirement, when implemented on conventional computers, hinders their usefulness in many critical problem areas e.g., genetic basis of diseases, macro finance, text classification, environment monitoring, etc. We propose a new non-von Neumann technology framework purposefully architected across all layers for solving these problems efficiently through physical equivalence, enabled by emerging nanotechnology. The architecture builds ...


Application Of Techniques For Map Estimation To Distributed Constraint Optimization Problem, Yoonheui Kim Jan 2015

Application Of Techniques For Map Estimation To Distributed Constraint Optimization Problem, Yoonheui Kim

Doctoral Dissertations

The problem of efficiently finding near-optimal decisions in multi-agent systems has become increasingly important because of the growing number of multi-agent applications with large numbers of agents operating in real-world environments. In these systems, agents are often subject to tight resource constraints and agents have only local views. When agents have non-global constraints, each of which is independent, the problem can be formalized as a distributed constraint optimization problem (DCOP). The DCOP is closely associated with the problem of inference on graphical models. Many approaches from inference literature have been adopted to solve DCOPs. We focus on the Max-Sum algorithm ...


Scalable Hardware Efficient Deep Spatio-Temporal Inference Networks, Steven Robert Young Dec 2014

Scalable Hardware Efficient Deep Spatio-Temporal Inference Networks, Steven Robert Young

Doctoral Dissertations

Deep machine learning (DML) is a promising field of research that has enjoyed much success in recent years. Two of the predominant deep learning architectures studied in the literature are Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs). Both have been successfully applied to many standard benchmarks with a primary focus on machine vision and speech processing domains.

Many real-world applications involve time-varying signals and, consequently, necessitate models that efficiently represent both temporal and spatial attributes. However, neither DBNs nor CNNs are designed to naturally capture temporal dependencies in observed data, often resulting in the inadequate transformation of spatio-temporal ...


Integrating Non-Topical Aspects Into Information Retrieval, Elif Aktolga May 2014

Integrating Non-Topical Aspects Into Information Retrieval, Elif Aktolga

Doctoral Dissertations

When users investigate a topic, they are often interested in results that are not just relevant, but also strongly opinionated or covering a range of times. To get such results, users are forced to formulate ambiguous, complex, or longer queries. Commonly this becomes a burden, since users need to issue several queries with reformulations if initial search results are not completely satisfactory. In this thesis, we focus on those two non-topical dimensions: opinionatedness and time. We develop measures for quantifying them in documents and incorporate them into search results.

For improving search results with respect to non-topical dimensions, we use ...


Feature Extraction And Recognition For Human Action Recognition, Jiajia Luo May 2014

Feature Extraction And Recognition For Human Action Recognition, Jiajia Luo

Doctoral Dissertations

How to automatically label videos containing human motions is the task of human action recognition. Traditional human action recognition algorithms use the RGB videos as input, and it is a challenging task because of the large intra-class variations of actions, cluttered background, possible camera movement, and illumination variations. Recently, the introduction of cost-effective depth cameras provides a new possibility to address difficult issues. However, it also brings new challenges such as noisy depth maps and time alignment. In this dissertation, effective and computationally efficient feature extraction and recognition algorithms are proposed for human action recognition.

At the feature extraction step ...


Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose Aug 2013

Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose

Doctoral Dissertations

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations ...


An Expert System For Guitar Sheet Music To Guitar Tablature, Chuanjun He May 2013

An Expert System For Guitar Sheet Music To Guitar Tablature, Chuanjun He

Doctoral Dissertations

This project applies analysis, design and implementation of the Optical Music Recognition (OMR) to an expert system for transforming guitar sheet music to guitar tablature. The first part includes image processing and music semantic interpretation to interpret and transform sheet music or printed scores into editable and playable electronic form. Then after importing the electronic form of music into internal data structures, our application uses effective pruning to explore the entire search space to find the best guitar tablature. Also considered are alternate guitar tunings and transposition of the music to improve the resulting tablature.


Extending Structural Learning Paradigms For High-Dimensional Machine Learning And Analysis, Christopher Todd Symons Dec 2012

Extending Structural Learning Paradigms For High-Dimensional Machine Learning And Analysis, Christopher Todd Symons

Doctoral Dissertations

Structure-based machine-learning techniques are frequently used in extensions of supervised learning, such as active, semi-supervised, multi-modal, and multi-task learning. A common step in many successful methods is a structure-discovery process that is made possible through the addition of new information, which can be user feedback, unlabeled data, data from similar tasks, alternate views of the problem, etc. Learning paradigms developed in the above-mentioned fields have led to some extremely flexible, scalable, and successful multivariate analysis approaches. This success and flexibility offer opportunities to expand the use of machine learning paradigms to more complex analyses. In particular, while information is often ...


Hard And Soft Error Resilience For One-Sided Dense Linear Algebra Algorithms, Peng Du Aug 2012

Hard And Soft Error Resilience For One-Sided Dense Linear Algebra Algorithms, Peng Du

Doctoral Dissertations

Dense matrix factorizations, such as LU, Cholesky and QR, are widely used by scientific applications that require solving systems of linear equations, eigenvalues and linear least squares problems. Such computations are normally carried out on supercomputers, whose ever-growing scale induces a fast decline of the Mean Time To Failure (MTTF). This dissertation develops fault tolerance algorithms for one-sided dense matrix factorizations, which handles Both hard and soft errors.

For hard errors, we propose methods based on diskless checkpointing and Algorithm Based Fault Tolerance (ABFT) to provide full matrix protection, including the left and right factor that are normally seen in ...


Deep Machine Learning With Spatio-Temporal Inference, Thomas Paul Karnowski May 2012

Deep Machine Learning With Spatio-Temporal Inference, Thomas Paul Karnowski

Doctoral Dissertations

Deep Machine Learning (DML) refers to methods which utilize hierarchies of more than one or two layers of computational elements to achieve learning. DML may draw upon biomemetic models, or may be simply biologically-inspired. Regardless, these architectures seek to employ hierarchical processing as means of mimicking the ability of the human brain to process a myriad of sensory data and make meaningful decisions based on this data. In this dissertation we present a novel DML architecture which is biologically-inspired in that (1) all processing is performed hierarchically; (2) all processing units are identical; and (3) processing captures both spatial and ...


Collaborative Solutions To Visual Sensor Networks, Mahmut Karakaya Aug 2011

Collaborative Solutions To Visual Sensor Networks, Mahmut Karakaya

Doctoral Dissertations

Visual sensor networks (VSNs) merge computer vision, image processing and wireless sensor network disciplines to solve problems in multi-camera applications in large surveillance areas. Although potentially powerful, VSNs also present unique challenges that could hinder their practical deployment because of the unique camera features including the extremely higher data rate, the directional sensing characteristics, and the existence of visual occlusions.

In this dissertation, we first present a collaborative approach for target localization in VSNs. Traditionally; the problem is solved by localizing targets at the intersections of the back-projected 2D cones of each target. However, the existence of visual occlusions among ...


Feature-Based Image Comparison And Its Application In Wireless Visual Sensor Networks, Yang Bai May 2011

Feature-Based Image Comparison And Its Application In Wireless Visual Sensor Networks, Yang Bai

Doctoral Dissertations

This dissertation studies the feature-based image comparison method and its application in Wireless Visual Sensor Networks.

Wireless Visual Sensor Networks (WVSNs), formed by a large number of low-cost, small-size visual sensor nodes, represent a new trend in surveillance and monitoring practices. Although each single sensor has very limited capability in sensing, processing and transmission, by working together they can achieve various high level tasks. Sensor collaboration is essential to WVSNs and normally performed among sensors having similar measurements, which are called neighbor sensors. The directional sensing characteristics of imagers and the presence of visual occlusion present unique challenges to neighborhood ...


Data Mining Based Learning Algorithms For Semi-Supervised Object Identification And Tracking, Michael P. Dessauer Jan 2011

Data Mining Based Learning Algorithms For Semi-Supervised Object Identification And Tracking, Michael P. Dessauer

Doctoral Dissertations

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify ...


Sub Pixel Analysis And Processing Of Sensor Data For Mobile Target Intelligence Information And Verification, Theresa Allen Williams Jan 2011

Sub Pixel Analysis And Processing Of Sensor Data For Mobile Target Intelligence Information And Verification, Theresa Allen Williams

Doctoral Dissertations

This dissertation introduces a novel process to study and analyze sensor data in order to obtain information pertaining to mobile targets at the sub-pixel level. The process design is modular in nature and utilizes a set of algorithmic tools for change detection, target extraction and analysis, super-pixel processing and target refinement. The scope of this investigation is confined to a staring sensor that records data of sub-pixel vehicles traveling horizontally across the ground. Statistical models of the targets and background are developed with noise and jitter effects. Threshold Change Detection, Duration Change Detection and Fast Adaptive Power Iteration (FAPI) Detection ...


A Novel Technique For Ctis Image-Reconstruction, Mitchel Dewayne Horton May 2010

A Novel Technique For Ctis Image-Reconstruction, Mitchel Dewayne Horton

Doctoral Dissertations

Computed tomography imaging spectrometer (CTIS) technology is introduced and its use is discussed. An iterative method is presented for CTIS image-reconstruction in the presence of both photon noise in the image and post-detection Gaussian system noise. The new algorithm assumes the transfer matrix of the system has a particular structure. Error analysis, performance evaluation, and parallelization of the algorithm is done. Complexity analysis is performed for the proof of concept code developed. Future work is discussed relating to potential improvements to the algorithm.

An intuitive explanation for the success of the new algorithm is that it reformulates the image reconstruction ...