Current Search: Detection (x)
Pages
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Title
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Learning Hierarchical Representations for Video Analysis Using Deep Learning.
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Creator
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Yang, Yang, Shah, Mubarak, Sukthankar, Gita, Da Vitoria Lobo, Niels, Stanley, Kenneth, Sukthankar, Rahul, University of Central Florida
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Abstract / Description
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With the exponential growth of the digital data, video content analysis (e.g., action, event recognition) has been drawing increasing attention from computer vision researchers. Effective modeling of the objects, scenes, and motions is critical for visual understanding. Recently there has been a growing interest in the bio-inspired deep learning models, which has shown impressive results in speech and object recognition. The deep learning models are formed by the composition of multiple non...
Show moreWith the exponential growth of the digital data, video content analysis (e.g., action, event recognition) has been drawing increasing attention from computer vision researchers. Effective modeling of the objects, scenes, and motions is critical for visual understanding. Recently there has been a growing interest in the bio-inspired deep learning models, which has shown impressive results in speech and object recognition. The deep learning models are formed by the composition of multiple non-linear transformations of the data, with the goal of yielding more abstract and ultimately more useful representations. The advantages of the deep models are three fold: 1) They learn the features directly from the raw signal in contrast to the hand-designed features. 2) The learning can be unsupervised, which is suitable for large data where labeling all the data is expensive and unpractical. 3) They learn a hierarchy of features one level at a time and the layerwise stacking of feature extraction, this often yields better representations.However, not many deep learning models have been proposed to solve the problems in video analysis, especially videos ``in a wild''. Most of them are either dealing with simple datasets, or limited to the low-level local spatial-temporal feature descriptors for action recognition. Moreover, as the learning algorithms are unsupervised, the learned features preserve generative properties rather than the discriminative ones which are more favorable in the classification tasks. In this context, the thesis makes two major contributions.First, we propose several formulations and extensions of deep learning methods which learn hierarchical representations for three challenging video analysis tasks, including complex event recognition, object detection in videos and measuring action similarity. The proposed methods are extensively demonstrated for each work on the state-of-the-art challenging datasets. Besides learning the low-level local features, higher level representations are further designed to be learned in the context of applications. The data-driven concept representations and sparse representation of the events are learned for complex event recognition; the representations for object body parts and structures are learned for object detection in videos; and the relational motion features and similarity metrics between video pairs are learned simultaneously for action verification.Second, in order to learn discriminative and compact features, we propose a new feature learning method using a deep neural network based on auto encoders. It differs from the existing unsupervised feature learning methods in two ways: first it optimizes both discriminative and generative properties of the features simultaneously, which gives our features a better discriminative ability. Second, our learned features are more compact, while the unsupervised feature learning methods usually learn a redundant set of over-complete features. Extensive experiments with quantitative and qualitative results on the tasks of human detection and action verification demonstrate the superiority of our proposed models.
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Date Issued
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2013
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Identifier
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CFE0004964, ucf:49593
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0004964
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Title
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ON THE APPLICATION OF LOCALITY TO NETWORK INTRUSION DETECTION: WORKING-SET ANALYSIS OF REAL AND SYNTHETIC NETWORK SERVER TRAFFIC.
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Creator
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Lee, Robert, Lang, Sheau-Dong, University of Central Florida
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Abstract / Description
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Keeping computer networks safe from attack requires ever-increasing vigilance. Our work on applying locality to network intrusion detection is presented in this dissertation. Network servers that allow connections from both the internal network and the Internet are vulnerable to attack from all sides. Analysis of the behavior of incoming connections for properties of locality can be used to create a normal profile for such network servers. Intrusions can then be detected due to their abnormal...
Show moreKeeping computer networks safe from attack requires ever-increasing vigilance. Our work on applying locality to network intrusion detection is presented in this dissertation. Network servers that allow connections from both the internal network and the Internet are vulnerable to attack from all sides. Analysis of the behavior of incoming connections for properties of locality can be used to create a normal profile for such network servers. Intrusions can then be detected due to their abnormal behavior. Data was collected from a typical network server both under normal conditions and under specific attacks. Experiments show that connections to the server do in fact exhibit locality, and attacks on the server can be detected through their violation of locality. Key to the detection of locality is a data structure called a working-set, which is a kind of cache of certain data related to network connections. Under real network conditions, we have demonstrated that the working-set behaves in a manner consistent with locality. Determining the reasons for this behavior is our next goal. A model that generates synthetic traffic based on actual network traffic allows us to study basic traffic characteristics. Simulation of working-set processing of the synthetic traffic shows that it behaves much like actual traffic. Attacks inserted into a replay of the synthetic traffic produce working-set responses similar to those produced in actual traffic. In the future, our model can be used to further the development of intrusion detection strategies.
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Date Issued
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2009
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Identifier
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CFE0002718, ucf:48171
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0002718
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Title
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Student Community Detection and Recommendation of Customized Paths to Reinforce Academic Success.
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Creator
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Shao, Yuan, Jha, Sumit Kumar, Zhang, Wei, Zhang, Shaojie, University of Central Florida
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Abstract / Description
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Educational Data Mining (EDM) is a research area that analyzes educational data and extracts interesting and unique information to address education issues. EDM implements computational methods to explore data for the purpose of studying questions related to educational achievements. A common task in an educational environment is the grouping of students and the identification of communities that have common features. Then, these communities of students may be studied by a course developer to...
Show moreEducational Data Mining (EDM) is a research area that analyzes educational data and extracts interesting and unique information to address education issues. EDM implements computational methods to explore data for the purpose of studying questions related to educational achievements. A common task in an educational environment is the grouping of students and the identification of communities that have common features. Then, these communities of students may be studied by a course developer to build a personalized learning system, promote effective group learning, provide adaptive contents, etc. The objective of this thesis is to find an approach to detect student communities and analyze students who do well academically with particular sequences of classes in each community. Then, we compute one or more sequences of courses that a student in a community may pursue to higher their chances of obtaining good academic performance.
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Date Issued
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2019
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Identifier
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CFE0007529, ucf:52623
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0007529
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Title
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COMPRESSIVE AND CODED CHANGE DETECTION: THEORY AND APPLICATION TO STRUCTURAL HEALTH MONITORING.
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Creator
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Sarayanibafghi, Omid, Atia, George, Vosoughi, Azadeh, Rahnavard, Nazanin, University of Central Florida
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Abstract / Description
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In traditional sparse recovery problems, the goal is to identify the support of compressible signals using a small number of measurements. In contrast, in this thesis the problem of identification of a sparse number of statistical changes in stochastic phenomena is considered when decision makers only have access to compressed measurements, i.e., each measurement is derived by a subset of features. Herein, we propose a new framework that is termed Compressed Change Detection. The main...
Show moreIn traditional sparse recovery problems, the goal is to identify the support of compressible signals using a small number of measurements. In contrast, in this thesis the problem of identification of a sparse number of statistical changes in stochastic phenomena is considered when decision makers only have access to compressed measurements, i.e., each measurement is derived by a subset of features. Herein, we propose a new framework that is termed Compressed Change Detection. The main approach relies on integrating ideas from the theory of identifying codes with change point detection in sequential analysis. If the stochastic properties of certain features change, then the changes can be detected by examining the covering set of an identifying code of measurements. In particular, given a large number N of features, the goal is to detect a small set of features that undergoes a statistical change using a small number of measurements. Sufficient conditions are derived for the probability of false alarm and isolation to approach zero in the asymptotic regime where N is large.As an application of compressed change detection, the problem of detection of a sparse number of damages in a structure for Structural Health Monitoring (SHM) is considered. Since only a small number of damage scenarios can occur simultaneously, change detection is applied to responses of pairs of sensors that form an identifying code over a learned damage-sensing graph. Generalizations of the proposed framework with multiple concurrent changes and for arbitrary graph topologies are presented.
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Date Issued
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2016
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Identifier
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CFE0006387, ucf:51507
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0006387
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Title
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Weakly Labeled Action Recognition and Detection.
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Creator
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Sultani, Waqas, Shah, Mubarak, Bagci, Ulas, Qi, GuoJun, Yun, Hae-Bum, University of Central Florida
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Abstract / Description
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Research in human action recognition strives to develop increasingly generalized methods thatare robust to intra-class variability and inter-class ambiguity. Recent years have seen tremendousstrides in improving recognition accuracy on ever larger and complex benchmark datasets, comprisingrealistic actions (")in the wild(") videos. Unfortunately, the all-encompassing, dense, globalrepresentations that bring about such improvements often benefit from the inherent characteristics,specific to...
Show moreResearch in human action recognition strives to develop increasingly generalized methods thatare robust to intra-class variability and inter-class ambiguity. Recent years have seen tremendousstrides in improving recognition accuracy on ever larger and complex benchmark datasets, comprisingrealistic actions (")in the wild(") videos. Unfortunately, the all-encompassing, dense, globalrepresentations that bring about such improvements often benefit from the inherent characteristics,specific to datasets and classes, that do not necessarily reflect knowledge about the entity to berecognized. This results in specific models that perform well within datasets but generalize poorly.Furthermore, training of supervised action recognition and detection methods need several precisespatio-temporal manual annotations to achieve good recognition and detection accuracy. For instance,current deep learning architectures require millions of accurately annotated videos to learnrobust action classifiers. However, these annotations are quite difficult to achieve.In the first part of this dissertation, we explore the reasons for poor classifier performance whentested on novel datasets, and quantify the effect of scene backgrounds on action representationsand recognition. We attempt to address the problem of recognizing human actions while trainingand testing on distinct datasets when test videos are neither labeled nor available during training. Inthis scenario, learning of a joint vocabulary, or domain transfer techniques are not applicable. Weperform different types of partitioning of the GIST feature space for several datasets and computemeasures of background scene complexity, as well as, for the extent to which scenes are helpfulin action classification. We then propose a new process to obtain a measure of confidence in eachpixel of the video being a foreground region using motion, appearance, and saliency together in a3D-Markov Random Field (MRF) based framework. We also propose multiple ways to exploit theforeground confidence: to improve bag-of-words vocabulary, histogram representation of a video,and a novel histogram decomposition based representation and kernel.iiiThe above-mentioned work provides probability of each pixel being belonging to the actor, however,it does not give the precise spatio-temporal location of the actor. Furthermore, above frameworkwould require precise spatio-temporal manual annotations to train an action detector. However,manual annotations in videos are laborious, require several annotators and contain humanbiases. Therefore, in the second part of this dissertation, we propose a weakly labeled approachto automatically obtain spatio-temporal annotations of actors in action videos. We first obtain alarge number of action proposals in each video. To capture a few most representative action proposalsin each video and evade processing thousands of them, we rank them using optical flow andsaliency in a 3D-MRF based framework and select a few proposals using MAP based proposal subsetselection method. We demonstrate that this ranking preserves the high-quality action proposals.Several such proposals are generated for each video of the same action. Our next challenge is toiteratively select one proposal from each video so that all proposals are globally consistent. Weformulate this as Generalized Maximum Clique Graph problem (GMCP) using shape, global andfine-grained similarity of proposals across the videos. The output of our method is the most actionrepresentative proposals from each video. Using our method can also annotate multiple instancesof the same action in a video can also be annotated. Moreover, action detection experiments usingannotations obtained by our method and several baselines demonstrate the superiority of ourapproach.The above-mentioned annotation method uses multiple videos of the same action. Therefore, inthe third part of this dissertation, we tackle the problem of spatio-temporal action localization in avideo, without assuming the availability of multiple videos or any prior annotations. The action islocalized by employing images downloaded from the Internet using action label. Given web images,we first dampen image noise using random walk and evade distracting backgrounds withinimages using image action proposals. Then, given a video, we generate multiple spatio-temporalaction proposals. We suppress camera and background generated proposals by exploiting opticalivflow gradients within proposals. To obtain the most action representative proposals, we propose toreconstruct action proposals in the video by leveraging the action proposals in images. Moreover,we preserve the temporal smoothness of the video and reconstruct all proposal bounding boxesjointly using the constraints that push the coefficients for each bounding box toward a commonconsensus, thus enforcing the coefficient similarity across multiple frames. We solve this optimizationproblem using the variant of two-metric projection algorithm. Finally, the video proposalthat has the lowest reconstruction cost and is motion salient is used to localize the action. Ourmethod is not only applicable to the trimmed videos, but it can also be used for action localizationin untrimmed videos, which is a very challenging problem.Finally, in the third part of this dissertation, we propose a novel approach to generate a few properlyranked action proposals from a large number of noisy proposals. The proposed approach beginswith dividing each proposal into sub-proposals. We assume that the quality of proposal remainsthe same within each sub-proposal. We, then employ a graph optimization method to recombinethe sub-proposals in all action proposals in a single video in order to optimally build new actionproposals and rank them by the combined node and edge scores. For an untrimmed video, we firstdivide the video into shots and then make the above-mentioned graph within each shot. Our methodgenerates a few ranked proposals that can be better than all the existing underlying proposals. Ourexperimental results validated that the properly ranked action proposals can significantly boostaction detection results.Our extensive experimental results on different challenging and realistic action datasets, comparisonswith several competitive baselines and detailed analysis of each step of proposed methodsvalidate the proposed ideas and frameworks.
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Date Issued
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2017
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Identifier
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CFE0006801, ucf:51809
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0006801
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Title
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Broad Bandwidth Optical Frequency Combs from Low Noise, High Repetition Rate Semiconductor Mode-Locked Lasers.
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Creator
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Klee, Anthony, Delfyett, Peter, Vanstryland, Eric, Schulzgen, Axel, DeSalvo, Richard, University of Central Florida
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Abstract / Description
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Mode-locked lasers have numerous applications in the areas of communications, spectroscopy, and frequency metrology. Harmonically mode-locked semiconductor lasers with external ring cavities offer a unique combination of benefits in that they can produce high repetition rate pulse trains with low timing jitter, achieve narrow axial mode linewidths, have the potential for entire monolithic integration on-chip, feature high wall-plug efficiency due to direct electrical pumping, and can be...
Show moreMode-locked lasers have numerous applications in the areas of communications, spectroscopy, and frequency metrology. Harmonically mode-locked semiconductor lasers with external ring cavities offer a unique combination of benefits in that they can produce high repetition rate pulse trains with low timing jitter, achieve narrow axial mode linewidths, have the potential for entire monolithic integration on-chip, feature high wall-plug efficiency due to direct electrical pumping, and can be engineered to operate in different wavelength bands of interest. However, lasers based on InP/InGaAsP quantum well devices which operate in the important telecom C-band have thus far been relatively limited in bandwidth as compared to competing platforms. Broad bandwidth is critical for increasing information carrying capacity and enabling femtosecond pulse production for coherent continuum generation in offset frequency stabilization. The goal of the work in this dissertation is to maximize the bandwidth of semiconductor lasers, bringing them closer to reaching their full potential as all-purpose sources.Dispersion in the laser cavity is a primary limiter of the achievable bandwidth in the laser architectures covered in this dissertation. In the first part of this dissertation, an accurate self-referenced technique based on multi-heterodyne detection is developed for measuring the spectral phase of a mode-locked laser. This technique is used to characterize the dispersion in several semiconductor laser architectures. In the second part, this knowledge is applied to reduce the dispersion in a laser cavity using a programmable pulse shaper, and thus increase the laser's spectral bandwidth. We demonstrate a 10 GHz frequency comb with bandwidth spanning 5 THz, representing a twofold improvement over the previously achievable bandwidth. Finally, this laser is converted to a stand-alone system by reconfiguring it as a coupled opto-electronic oscillator and a novel stabilization scheme is presented.
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Date Issued
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2016
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Identifier
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CFE0006129, ucf:51184
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0006129
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Title
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Suction Detection and Feedback Control for the Rotary Left Ventricular Assist Device.
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Creator
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Wang, Yu, Simaan, Marwan, Qu, Zhihua, Haralambous, Michael, Kassab, Alain, Divo, Eduardo, University of Central Florida
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Abstract / Description
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The Left Ventricular Assist Device (LVAD) is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. The rotary type pumps are controlled by varying the pump motor current to adjust the amount of blood flowing through the LVAD. One important challenge in using such a device is the desire to provide the patient with as close to a normal lifestyle as possible until a donor heart becomes available...
Show moreThe Left Ventricular Assist Device (LVAD) is a rotary mechanical pump that is implanted in patients with congestive heart failure to help the left ventricle in pumping blood in the circulatory system. The rotary type pumps are controlled by varying the pump motor current to adjust the amount of blood flowing through the LVAD. One important challenge in using such a device is the desire to provide the patient with as close to a normal lifestyle as possible until a donor heart becomes available. The development of an appropriate feedback controller that is capable of automatically adjusting the pump current is therefore a crucial step in meeting this challenge. In addition to being able to adapt to changes in the patient's daily activities, the controller must be able to prevent the occurrence of excessive pumping of blood from the left ventricle (a phenomenon known as ventricular suction) that may cause collapse of the left ventricle and damage to the heart muscle and tissues.In this dissertation, we present a new suction detection system that can precisely classify pump flow patterns, based on a Lagrangian Support Vector Machine (LSVM) model that combines six suction indices extracted from the pump flow signal to make a decision about whether the pump is not in suction, approaching suction, or in suction. The proposed method has been tested using in vivo experimental data based on two different LVAD pumps. The results show that the system can produce superior performance in terms of classification accuracy, stability, learning speed, and good robustness compared to three other existing suction detection methods and the original SVM-based algorithm. The ability of the proposed algorithm to detect suction provides a reliable platform for the development of a feedback control system to control the current of the pump (input variable) while at the same time ensuring that suction is avoided.Based on the proposed suction detector, a new control system for the rotary LVAD was developed to automatically regulate the pump current of the device to avoid ventricular suction. The control system consists of an LSVM suction detector and a feedback controller. The LSVM suction detector is activated first so as to correctly classify the pump status as No Suction (NS) or Suction (S). When the detection is (")No Suction("), the feedback controller is activated so as to automatically adjust the pump current in order that the blood flow requirements of the patient's body at different physiological states are met according to the patient's activity level. When the detection is (")Suction("), the pump current is immediately decreased in order to drive the pump back to a normal No Suction operating condition. The performance of the control system was tested in simulations over a wide range of physiological conditions.
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Date Issued
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2013
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Identifier
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CFE0005070, ucf:49956
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0005070
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Title
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Photonic Filtering for Applications in Microwave Generation and Metrology.
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Creator
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Bagnell, Marcus, Delfyett, Peter, Schoenfeld, Winston, Li, Guifang, Peale, Robert, University of Central Florida
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Abstract / Description
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This work uses the photonic filtering properties of Fabry-Perot etalons to show improvements in the electrical signals created upon photodetection of the optical signal. First, a method of delay measurement is described which uses multi-heterodyne detection to find correlations in white light signals at 20 km of delay to sub millimeter resolution. By filtering incoming white light with a Fabry-Perot etalon, the pseudo periodic signal is suitable for measurement by combining and photodetecting...
Show moreThis work uses the photonic filtering properties of Fabry-Perot etalons to show improvements in the electrical signals created upon photodetection of the optical signal. First, a method of delay measurement is described which uses multi-heterodyne detection to find correlations in white light signals at 20 km of delay to sub millimeter resolution. By filtering incoming white light with a Fabry-Perot etalon, the pseudo periodic signal is suitable for measurement by combining and photodetecting it with an optical frequency comb. In this way, optical data from a large bandwidth can be downconverted and sampled on low frequency electronics. Second, a high finesse etalon is used as a photonic filter inside an optoelectronic oscillator (OEO). The etalon's narrow filter function allows the OEO loop length to be extremely long for a high oscillator quality factor while still suppressing unwanted modes below the noise floor. The periodic nature of the etalon allows it to be used to generate a wide range of microwave and millimeter wave tones without degradation of the RF signal.
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Date Issued
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2014
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Identifier
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CFE0005457, ucf:50396
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0005457
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Title
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Visual Analysis of Extremely Dense Crowded Scenes.
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Creator
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Idrees, Haroon, Shah, Mubarak, Da Vitoria Lobo, Niels, Stanley, Kenneth, Atia, George, Saleh, Bahaa, University of Central Florida
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Abstract / Description
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Visual analysis of dense crowds is particularly challenging due to large number of individuals, occlusions, clutter, and fewer pixels per person which rarely occur in ordinary surveillance scenarios. This dissertation aims to address these challenges in images and videos of extremely dense crowds containing hundreds to thousands of humans. The goal is to tackle the fundamental problems of counting, detecting and tracking people in such images and videos using visual and contextual cues that...
Show moreVisual analysis of dense crowds is particularly challenging due to large number of individuals, occlusions, clutter, and fewer pixels per person which rarely occur in ordinary surveillance scenarios. This dissertation aims to address these challenges in images and videos of extremely dense crowds containing hundreds to thousands of humans. The goal is to tackle the fundamental problems of counting, detecting and tracking people in such images and videos using visual and contextual cues that are automatically derived from the crowded scenes.For counting in an image of extremely dense crowd, we propose to leverage multiple sources of information to compute an estimate of the number of individuals present in the image. Our approach relies on sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Furthermore, we employ a global consistency constraint on counts using Markov Random Field which caters for disparity in counts in local neighborhoods and across scales. We tested this approach on crowd images with the head counts ranging from 94 to 4543 and obtained encouraging results. Through this approach, we are able to count people in images of high-density crowds unlike previous methods which are only applicable to videos of low to medium density crowded scenes. However, the counting procedure just outputs a single number for a large patch or an entire image. With just the counts, it becomes difficult to measure the counting error for a query image with unknown number of people. For this, we propose to localize humans by finding repetitive patterns in the crowd image. Starting with detections from an underlying head detector, we correlate them within the image after their selection through several criteria: in a pre-defined grid, locally, or at multiple scales by automatically finding the patches that are most representative of recurring patterns in the crowd image. Finally, the set of generated hypotheses is selected using binary integer quadratic programming with Special Ordered Set (SOS) Type 1 constraints.Human Detection is another important problem in the analysis of crowded scenes where the goal is to place a bounding box on visible parts of individuals. Primarily applicable to images depicting medium to high density crowds containing several hundred humans, it is a crucial pre-requisite for many other visual tasks, such as tracking, action recognition or detection of anomalous behaviors, exhibited by individuals in a dense crowd. For detecting humans, we explore context in dense crowds in the form of locally-consistent scale prior which captures the similarity in scale in local neighborhoods with smooth variation over the image. Using the scale and confidence of detections obtained from an underlying human detector, we infer scale and confidence priors using Markov Random Field. In an iterative mechanism, the confidences of detections are modified to reflect consistency with the inferred priors, and the priors are updated based on the new detections. The final set of detections obtained are then reasoned for occlusion using Binary Integer Programming where overlaps and relations between parts of individuals are encoded as linear constraints. Both human detection and occlusion reasoning in this approach are solved with local neighbor-dependent constraints, thereby respecting the inter-dependence between individuals characteristic to dense crowd analysis. In addition, we propose a mechanism to detect different combinations of body parts without requiring annotations for individual combinations.Once human detection and localization is performed, we then use it for tracking people in dense crowds. Similar to the use of context as scale prior for human detection, we exploit it in the form of motion concurrence for tracking individuals in dense crowds. The proposed method for tracking provides an alternative and complementary approach to methods that require modeling of crowd flow. Simultaneously, it is less likely to fail in the case of dynamic crowd flows and anomalies by minimally relying on previous frames. The approach begins with the automatic identification of prominent individuals from the crowd that are easy to track. Then, we use Neighborhood Motion Concurrence to model the behavior of individuals in a dense crowd, this predicts the position of an individual based on the motion of its neighbors. When the individual moves with the crowd flow, we use Neighborhood Motion Concurrence to predict motion while leveraging five-frame instantaneous flow in case of dynamically changing flow and anomalies. All these aspects are then embedded in a framework which imposes hierarchy on the order in which positions of individuals are updated. The results are reported on eight sequences of medium to high density crowds and our approach performs on par with existing approaches without learning or modeling patterns of crowd flow.We experimentally demonstrate the efficacy and reliability of our algorithms by quantifying the performance of counting, localization, as well as human detection and tracking on new and challenging datasets containing hundreds to thousands of humans in a given scene.
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Date Issued
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2014
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Identifier
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CFE0005508, ucf:50367
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0005508
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Title
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Zinc Sulfide:manganese doped Quantum rods for detection of metal ions and a business model for future sales.
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Creator
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Teblum, Andrew, Santra, Swadeshmukul, Gesquiere, Andre, Soskin, Mark, University of Central Florida
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Abstract / Description
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Hexavalent chromium is an extremely carcinogenic chemical that has been widely produced in the United States. This has led to major waste contamination and pollution throughout the country. According to the Environmental Working Group Hexavalent chromium has been found in 89% of city tap water. Most people believe they are safe using regular home filter systems however that is not true. A more expensive ion exchange water treatment unit is required. Therefore to protect yourselves from this...
Show moreHexavalent chromium is an extremely carcinogenic chemical that has been widely produced in the United States. This has led to major waste contamination and pollution throughout the country. According to the Environmental Working Group Hexavalent chromium has been found in 89% of city tap water. Most people believe they are safe using regular home filter systems however that is not true. A more expensive ion exchange water treatment unit is required. Therefore to protect yourselves from this carcinogenic metal a reliable test is required. In this study we have developed a Zinc Sulfide Manganese doped Quantum Rod technology to detect for presence of chromate and other harmful transitional metals in drinking water. Quantum Rods were synthesized using a hydrothermal reaction method. They were fully characterized using UV-visible absorption spectroscopy, fluorescence emission spectroscopy, X-ray Photoelectric Spectroscopy (XPS) and High Resolution Transmission Electron Microscopy (HRTEM). Quantum Rod metal detection studies were done with 28 different ions in a 96-well fluorescent plate reader. Results show that highest sensitivity to 8 ions including the toxic ions of chromate and mercury allowing us to create a sensor to detect these items.
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Date Issued
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2014
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Identifier
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CFE0005268, ucf:50569
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0005268
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Title
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UNSUPERVISED BUILDING DETECTION FROM IRREGULARLY SPACED LIDAR AND AERIAL IMAGERY.
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Creator
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Shorter, Nicholas, Kasparis, Takis, University of Central Florida
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Abstract / Description
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As more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile...
Show moreAs more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile communication (cell phone tower placement), spatial analysis of air pollution and noise nuisances, microclimate investigations, geographical information systems, security services and change detection from areas affected by natural disasters. Building detection and reconstruction are also used in the military for automatic target recognition and in entertainment for virtual tourism. Previously proposed building detection and reconstruction algorithms solely utilized aerial imagery. With the advent of Light Detection and Ranging (LiDAR) systems providing elevation data, current algorithms explore using captured LiDAR data as an additional feasible source of information. Additional sources of information can lead to automating techniques (alleviating their need for manual user intervention) as well as increasing their capabilities and accuracy. Several building detection approaches surveyed in the open literature have fundamental weaknesses that hinder their use; such as requiring multiple data sets from different sensors, mandating certain operations to be carried out manually, and limited functionality to only being able to detect certain types of buildings. In this work, a building detection system is proposed and implemented which strives to overcome the limitations seen in existing techniques. The developed framework is flexible in that it can perform building detection from just LiDAR data (first or last return), or just nadir, color aerial imagery. If data from both LiDAR and aerial imagery are available, then the algorithm will use them both for improved accuracy. Additionally, the proposed approach does not employ severely limiting assumptions thus enabling the end user to apply the approach to a wider variety of different building types. The proposed approach is extensively tested using real data sets and it is also compared with other existing techniques. Experimental results are presented.
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Date Issued
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2009
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Identifier
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CFE0002783, ucf:48125
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0002783
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Title
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Batch and Online Implicit Weighted Gaussian Processes for Robust Novelty Detection.
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Creator
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Ramirez Padron, Ruben, Gonzalez, Avelino, Georgiopoulos, Michael, Stanley, Kenneth, Mederos, Boris, Wang, Chung-Ching, University of Central Florida
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Abstract / Description
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This dissertation aims mainly at obtaining robust variants of Gaussian processes (GPs) that do not require using non-Gaussian likelihoods to compensate for outliers in the training data. Bayesian kernel methods, and in particular GPs, have been used to solve a variety of machine learning problems, equating or exceeding the performance of other successful techniques. That is the case of a recently proposed approach to GP-based novelty detection that uses standard GPs (i.e. GPs employing...
Show moreThis dissertation aims mainly at obtaining robust variants of Gaussian processes (GPs) that do not require using non-Gaussian likelihoods to compensate for outliers in the training data. Bayesian kernel methods, and in particular GPs, have been used to solve a variety of machine learning problems, equating or exceeding the performance of other successful techniques. That is the case of a recently proposed approach to GP-based novelty detection that uses standard GPs (i.e. GPs employing Gaussian likelihoods). However, standard GPs are sensitive to outliers in training data, and this limitation carries over to GP-based novelty detection. This limitation has been typically addressed by using robust non-Gaussian likelihoods. However, non-Gaussian likelihoods lead to analytically intractable inferences, which require using approximation techniques that are typically complex and computationally expensive. Inspired by the use of weights in quasi-robust statistics, this work introduces a particular type of weight functions, called here data weighers, in order to obtain robust GPs that do not require approximation techniques and retain the simplicity of standard GPs. This work proposes implicit weighted variants of batch GP, online GP, and sparse online GP (SOGP) that employ weighted Gaussian likelihoods. Mathematical expressions for calculating the posterior implicit weighted GPs are derived in this work. In our experiments, novelty detection based on our weighted batch GPs consistently and significantly outperformed standard batch GP-based novelty detection whenever data was contaminated with outliers. Additionally, our experiments show that novelty detection based on online GPs can perform similarly to batch GP-based novelty detection. Membership scores previously introduced by other authors are also compared in our experiments.
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Date Issued
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2015
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Identifier
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CFE0005869, ucf:50858
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0005869
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Title
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SCENE MONITORING WITH A FOREST OF COOPERATIVE SENSORS.
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Creator
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Javed, Omar, Shah, Mubarak, University of Central Florida
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Abstract / Description
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In this dissertation, we present vision based scene interpretation methods for monitoring of people and vehicles, in real-time, within a busy environment using a forest of co-operative electro-optical (EO) sensors. We have developed novel video understanding algorithms with learning capability, to detect and categorize people and vehicles, track them with in a camera and hand-off this information across multiple networked cameras for multi-camera tracking. The ability to learn prevents the...
Show moreIn this dissertation, we present vision based scene interpretation methods for monitoring of people and vehicles, in real-time, within a busy environment using a forest of co-operative electro-optical (EO) sensors. We have developed novel video understanding algorithms with learning capability, to detect and categorize people and vehicles, track them with in a camera and hand-off this information across multiple networked cameras for multi-camera tracking. The ability to learn prevents the need for extensive manual intervention, site models and camera calibration, and provides adaptability to changing environmental conditions. For object detection and categorization in the video stream, a two step detection procedure is used. First, regions of interest are determined using a novel hierarchical background subtraction algorithm that uses color and gradient information for interest region detection. Second, objects are located and classified from within these regions using a weakly supervised learning mechanism based on co-training that employs motion and appearance features. The main contribution of this approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The advantage of this approach is that it requires only a few initial training samples and can automatically adjust its parameters online to improve the detection and classification performance. Once objects are detected and classified they are tracked in individual cameras. Single camera tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence in individual cameras. The tracker has the capability to handle multiple occluded objects. Next, the objects are tracked across a forest of cameras with non-overlapping views. This is a hard problem because of two reasons. First, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in illumination, pose and camera properties. To deal with the first problem, the system learns the inter-camera relationships to constrain track correspondences. These relationships are learned in the form of multivariate probability density of space-time variables (object entry and exit locations, velocities, and inter-camera transition times) using Parzen windows. To handle the appearance change of an object as it moves from one camera to another, we show that all color transfer functions from a given camera to another camera lie in a low dimensional subspace. The tracking algorithm learns this subspace by using probabilistic principal component analysis and uses it for appearance matching. The proposed system learns the camera topology and subspace of inter-camera color transfer functions during a training phase. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both the location and appearance cues. Extensive experiments and deployment of this system in realistic scenarios has demonstrated the robustness of the proposed methods. The proposed system was able to detect and classify targets, and seamlessly tracked them across multiple cameras. It also generated a summary in terms of key frames and textual description of trajectories to a monitoring officer for final analysis and response decision. This level of interpretation was the goal of our research effort, and we believe that it is a significant step forward in the development of intelligent systems that can deal with the complexities of real world scenarios.
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Date Issued
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2005
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Identifier
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CFE0000497, ucf:46362
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0000497
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Title
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HIGH PERFORMANCE DATA MINING TECHNIQUES FOR INTRUSION DETECTION.
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Creator
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Siddiqui, Muazzam Ahmed, Lee, Joohan, University of Central Florida
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Abstract / Description
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The rapid growth of computers transformed the way in which information and data was stored. With this new paradigm of data access, comes the threat of this information being exposed to unauthorized and unintended users. Many systems have been developed which scrutinize the data for a deviation from the normal behavior of a user or system, or search for a known signature within the data. These systems are termed as Intrusion Detection Systems (IDS). These systems employ different techniques...
Show moreThe rapid growth of computers transformed the way in which information and data was stored. With this new paradigm of data access, comes the threat of this information being exposed to unauthorized and unintended users. Many systems have been developed which scrutinize the data for a deviation from the normal behavior of a user or system, or search for a known signature within the data. These systems are termed as Intrusion Detection Systems (IDS). These systems employ different techniques varying from statistical methods to machine learning algorithms.Intrusion detection systems use audit data generated by operating systems, application softwares or network devices. These sources produce huge amount of datasets with tens of millions of records in them. To analyze this data, data mining is used which is a process to dig useful patterns from a large bulk of information. A major obstacle in the process is that the traditional data mining and learning algorithms are overwhelmed by the bulk volume and complexity of available data. This makes these algorithms impractical for time critical tasks like intrusion detection because of the large execution time.Our approach towards this issue makes use of high performance data mining techniques to expedite the process by exploiting the parallelism in the existing data mining algorithms and the underlying hardware. We will show that how high performance and parallel computing can be used to scale the data mining algorithms to handle large datasets, allowing the data mining component to search a much larger set of patterns and models than traditional computational platforms and algorithms would allow.We develop parallel data mining algorithms by parallelizing existing machine learning techniques using cluster computing. These algorithms include parallel backpropagation and parallel fuzzy ARTMAP neural networks. We evaluate the performances of the developed models in terms of speedup over traditional algorithms, prediction rate and false alarm rate. Our results showed that the traditional backpropagation and fuzzy ARTMAP algorithms can benefit from high performance computing techniques which make them well suited for time critical tasks like intrusion detection.
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Date Issued
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2004
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Identifier
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CFE0000056, ucf:46142
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0000056
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Title
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STRUCTURAL HEALTH MONITORING OF COMPOSITE OVERWRAPPED PRESSURE VESSELS.
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Creator
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Letizia, Luca, Catbas, F. Necati, University of Central Florida
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Abstract / Description
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This work is focusing to study the structural behavior of Composite Overwrapped Pressure Vessels (COPVs). These COPVs are found in many engineering applications. In the aerospace field, they are installed onto spaceships and aid the reorientation of the spacecraft in very far and airless, therefore frictionless, orbits to save energy and fuel. The intent of this research is to analyze the difference in performance of both perfectly intact and purposely damaged tanks. Understanding both the...
Show moreThis work is focusing to study the structural behavior of Composite Overwrapped Pressure Vessels (COPVs). These COPVs are found in many engineering applications. In the aerospace field, they are installed onto spaceships and aid the reorientation of the spacecraft in very far and airless, therefore frictionless, orbits to save energy and fuel. The intent of this research is to analyze the difference in performance of both perfectly intact and purposely damaged tanks. Understanding both the source and location of a structural fault will help NASA engineers predict the performance of COPVs subject to similar conditions, which could prevent failures of important missions. The structural behavior of six tanks is investigated by means of experimental modal analysis. Knowledge of statistical signal processing methods allows to sort out and extract meaningful features from the data as to gain understanding of the performance of the structures. Structural identification is carried out using Narrow Band and Broad Band algorithms. A comparison through correlation tables and figures presents the differences in natural frequencies, mode shapes and damping ratios of all structures. A careful analysis displays the deviation of these modal parameters in the damaged tanks, highlighting the evident structural defects.
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Date Issued
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2016
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Identifier
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CFH2000069, ucf:45514
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFH2000069
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Title
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USING ANTENNA TILE-ASSISTED SUBSTRATE DELIVERY TO IMPROVE THE DETECTION LIMITS OF DEOXYRIBOZYME BIOSENSORS.
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Creator
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Cox, Amanda, Kolpashchikov, Dmitry, University of Central Florida
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Abstract / Description
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One common limitation of enzymatic reactions is the diffusion of a substrate to the enzyme active site and/or the release of the reaction products. These reactions are known as diffusion-controlled. Overcoming this limitation may enable faster catalytic rates, which in the case of catalytic biosensors can potentially lower limits of detection of specific analyte. Here we created an artificial system to enable deoxyribozyme (Dz) 10-23 based biosensor to overcome its diffusion limit. The sensor...
Show moreOne common limitation of enzymatic reactions is the diffusion of a substrate to the enzyme active site and/or the release of the reaction products. These reactions are known as diffusion-controlled. Overcoming this limitation may enable faster catalytic rates, which in the case of catalytic biosensors can potentially lower limits of detection of specific analyte. Here we created an artificial system to enable deoxyribozyme (Dz) 10-23 based biosensor to overcome its diffusion limit. The sensor consists of the two probe strands, which bind to the analyzed nucleic acid by Watson-Crick base pairs and, upon binding re-form the catalytic core of Dz 10-23. The activated Dz 10-23 cleaves the fluorophore and quencher-labeled DNA-RNA substrate which separates the fluorophore from the quencher thus producing high fluorescent signal. This system uses a Dz 10-23 biosensor strand associated to a DNA antenna tile, which captures the fluorogenic substrate and channels it to the reaction center where the Dz 10-23 cleaves the substrate. DNA antenna tile captures fluorogenic substrate and delivers it to the activated Dz 10-23 core. This allows for lower levels of analyte to be detected without compromising the specificity of the biosensor. The results of this experiment demonstrated that using DNA antenna, we can create a synthetic environment around the Dz 10-23 biosensor to increase its efficiency and allow for lower levels of analyte to be detected without using amplification techniques like PCR.
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Date Issued
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2015
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Identifier
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CFH0004887, ucf:45432
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFH0004887
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Title
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SQUARAINE DYES FOR TWO-PHOTON FLUORESCENCE BIOIMAGING APPLICATIONS.
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Creator
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Colon Gomez, Maria, Belfield, Kevin, University of Central Florida
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Abstract / Description
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Near-infrared emitting squaraine dyes are promising candidates for bioimaging applications. Two-photon fluorescence microscopy (2PFM) imaging is a powerful tool being used for studying biological function since it produces 3D images with minimal damage to cells and lower fluorophore photobleaching. The fluorescence wavelength of squaraine dyes normally falls in the near infrared region, providing deeper penetration through biological samples such as thick tissue sections. Squaraine dyes that...
Show moreNear-infrared emitting squaraine dyes are promising candidates for bioimaging applications. Two-photon fluorescence microscopy (2PFM) imaging is a powerful tool being used for studying biological function since it produces 3D images with minimal damage to cells and lower fluorophore photobleaching. The fluorescence wavelength of squaraine dyes normally falls in the near infrared region, providing deeper penetration through biological samples such as thick tissue sections. Squaraine dyes that could work for imaging cells and tissues for 2PFM imaging were synthesized and underwent comprehensive photophysical characterization, such as UV-Vis absorption, fluorescence, and anisotropy. The squaraine dyes were tested for cell toxicity to determine the concentration at which the cells should be incubated with the dye for 2PFM. In addition, the squaraine dyes were incubated with cancer cells to evaluate their utility in the bioimaging process. The squaraine dye that is not soluble in water can be incorporated in silica nanoparticles or micelles to facilitate dispersal in water for evaluation of its use as a probe. The prospective squaraine dyes can be used in cells and tissues for imaging that can then be analyzed to ascertain its use as a probe for biomedical applications, such as early cancer detection.
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Date Issued
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2013
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Identifier
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CFH0004338, ucf:45020
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFH0004338
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Title
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Do multiple conditions elicit the visual redundant signals effect in simple response times?.
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Creator
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Mishler, Ada, Neider, Mark, Lighthall, Nichole, Szalma, James, Joseph, Dana, University of Central Florida
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Abstract / Description
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The redundant signals effect, or redundancy gain, is an increase in human processing efficiency when target redundancy is introduced into a display. An advantage for two visual signals over one has been found in a wide variety of speeded response time tasks, but does not always occur and may be weakened by some task parameters. These disparate results suggest that visual redundancy gain is not a unitary effect, but is instead based on different underlying mechanisms in different tasks. The...
Show moreThe redundant signals effect, or redundancy gain, is an increase in human processing efficiency when target redundancy is introduced into a display. An advantage for two visual signals over one has been found in a wide variety of speeded response time tasks, but does not always occur and may be weakened by some task parameters. These disparate results suggest that visual redundancy gain is not a unitary effect, but is instead based on different underlying mechanisms in different tasks. The current study synthesizes previous theories applied to redundancy gain into the three-conditions hypothesis, which states that visual redundancy gain depends on the presence of at least one of three factors: visual identicalness between multiple targets, familiarity with multiple similar targets, or prepotentiation for multiple different targets. In a series of four simple response time experiments, participants responded to single targets presented to one side of the visual field, or to bilateral targets presented to both sides of the visual field. The first three experiments each explored one condition, the first experiment by comparing identical to non-identical random shapes to examine visual identicalness, the second by comparing familiar to unfamiliar letters to examine familiarity, and the third by comparing previewed with non-previewed random shapes to examine prepotentiation. Finally, the fourth experiment employed letters that varied in familiarity, identicalness, and preview, to examine whether or not the three hypothesized causes have multiplicative effects on redundancy. Results indicated that participants were able to benefit equally from redundancy regardless of identicalness, familiarity, or prepotentiation, but that they did so by ignoring one target in the redundant-target trials. These results suggest that redundancy gain may need to be even further divided into more than three underlying mechanisms, with a serial processing mechanism that can be used for stimuli that are not familiar, prepotentiated, or identical.
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Date Issued
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2017
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Identifier
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CFE0006899, ucf:52890
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0006899
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Title
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Engineering Noble-metal Nanostructures for Biosensing Applications.
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Creator
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Ye, Haihang, Xia, Xiaohu, Kuebler, Stephen, Chen, Gang, Beazley, Melanie, Feng, Xiaofeng, University of Central Florida
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Abstract / Description
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The ability to engineer noble-metal nanostructures (NMNSs) in a controllable manner and to understand the structure-dependent properties greatly boost our knowledge in rational design of biosensing technologies. In particular, as a type of highly efficient peroxidase mimics, NMNSs hold promising potential to break through the bottleneck of conventional enzyme-based in vitro diagnostics.During the time of my Ph.D. study, I have successfully: 1) directed a two-step method involving seed...
Show moreThe ability to engineer noble-metal nanostructures (NMNSs) in a controllable manner and to understand the structure-dependent properties greatly boost our knowledge in rational design of biosensing technologies. In particular, as a type of highly efficient peroxidase mimics, NMNSs hold promising potential to break through the bottleneck of conventional enzyme-based in vitro diagnostics.During the time of my Ph.D. study, I have successfully: 1) directed a two-step method involving seed-mediated growth and chemical etching for the synthesis of Ru nanoframes (RuNFs) with face-centered cubic crystal phase and enhanced catalytic activities; 2) demonstrated, for the first time, the inherent peroxidase-like activity of RuNFs as a type of efficient peroxidase mimics, opening up possibilities for their bioapplications; 3) developed an enzyme-free signal amplification technique for ultrasensitive colorimetric assay of disease biomarkers by using Pd-Ir nanooctahedra encapsulated gold vesicles as labels; 4) prepared polyvinylpyrrolidone (PVP)-capped Pt nanocubes with superior peroxidase-like catalytic activity and record-high specific catalytic activity; 5) developed a facile colorimetric method for the detection of Ag(I) ions with picomolar sensitivity by using the PVP-capped Pt nanocubes as the probes; 6) developed a non-enzyme cascade amplification strategy for colorimetric assay of disease biomarkers by taking advantage of the interaction between the Ag(I) ions and PVP-capped Pt nanocubes; and 7) established a highly sensitive colorimetric lateral flow assay platform by using Au@Pt core-shell nanoparticles as the labels that possess both plasmonic and catalytic properties.
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Date Issued
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2019
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Identifier
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CFE0007559, ucf:52626
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0007559
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Title
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X-ray Radiation Enabled Cancer Detection and Treatment with Nanoparticles.
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Creator
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Hossain, Mainul, Su, Ming, Behal, Aman, Gong, Xun, Hu, Haiyan, Kapoor, Vikram, Deng, Weiwei, University of Central Florida
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Abstract / Description
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Despite significant improvements in medical sciences over the last decade, cancer still continues to be a major cause of death in humans throughout the world. Parallel to the efforts of understanding the intricacies of cancer biology, researchers are continuously striving to develop effective cancer detection and treatment strategies. Use of nanotechnology in the modern era opens up a wide range of possibilities for diagnostics, therapies and preventive measures for cancer management....
Show moreDespite significant improvements in medical sciences over the last decade, cancer still continues to be a major cause of death in humans throughout the world. Parallel to the efforts of understanding the intricacies of cancer biology, researchers are continuously striving to develop effective cancer detection and treatment strategies. Use of nanotechnology in the modern era opens up a wide range of possibilities for diagnostics, therapies and preventive measures for cancer management. Although, existing strategies of cancer detection and treatment, using nanoparticles, have been proven successful in case of cancer imaging and targeted drug deliveries, they are often limited by poor sensitivity, lack of specificity, complex sample preparation efforts and inherent toxicities associated with the nanoparticles, especially in case of in-vivo applications. Moreover, the detection of cancer is not necessarily integrated with treatment. X-rays have long been used in radiation therapy to kill cancer cells and also for imaging tumors inside the body using nanoparticles as contrast agents. However, X-rays, in combination with nanoparticles, can also be used for cancer diagnosis by detecting cancer biomarkers and circulating tumor cells. Moreover, the use of nanoparticles can also enhance the efficacy of X-ray radiation therapy for cancer treatment.This dissertation describes a novel in vitro technique for cancer detection and treatment using X-ray radiation and nanoparticles. Surfaces of synthesized metallic nanoparticles have been modified with appropriate ligands to specifically target cancer cells and biomarkers in vitro. Characteristic X-ray fluorescence signals from the X-ray irradiated nanoparticles are then used for detecting the presence of cancer. The method enables simultaneous detection of multiple cancer biomarkers allowing accurate diagnosis and early detection of cancer. Circulating tumor cells, which are the primary indicators of cancer metastasis, have also been detected where the use of magnetic nanoparticles allows enrichment of rare cancer cells prior to detection. The approach is unique in that it integrates cancer detection and treatment under one platform, since, X-rays have been shown to effectively kill cancer cells through radiation induced DNA damage. Due to high penetrating power of X-rays, the method has potential applications for in vivo detection and treatment of deeply buried cancers in humans. The effect of nanoparticle toxicity on multiple cell types has been investigated using conventional cytotoxicity assays for both unmodified nanoparticles as well as nanoparticles modified with a variety of surface coatings. Appropriate surface modifications have significantly reduced inherent toxicity of nanoparticles, providing possibilities for future clinical applications. To investigate cellular damages caused by X-ray radiation, an on-chip biodosimeter has been fabricated based on three dimensional microtissues which allows direct monitoring of responses to X-ray exposure for multiple mammalian cell types. Damage to tumor cells caused by X-rays is known to be significantly higher in presence of nanoparticles which act as radiosensitizers and enhance localized radiation doses. An analytical approach is used to investigate the various parameters that affect the radiosensitizing properties of the nanoparticles. The results can be used to increase the efficacy of nanoparticle aided X-ray radiation therapy for cancer treatment by appropriate choice of X-ray beam energy, nanoparticle size, material composition and location of nanoparticle with respect to the tumor cell nucleus.
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Date Issued
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2012
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Identifier
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CFE0004547, ucf:49242
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Format
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Document (PDF)
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PURL
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http://purl.flvc.org/ucf/fd/CFE0004547
Pages